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annotate venv/lib/python2.7/site-packages/boto/dynamodb2/table.py @ 0:d67268158946 draft

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planemo upload commit a3f181f5f126803c654b3a66dd4e83a48f7e203b
author bcclaywell
date Mon, 12 Oct 2015 17:43:33 -0400
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1 import boto
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2 from boto.dynamodb2 import exceptions
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3 from boto.dynamodb2.fields import (HashKey, RangeKey,
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4 AllIndex, KeysOnlyIndex, IncludeIndex,
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5 GlobalAllIndex, GlobalKeysOnlyIndex,
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6 GlobalIncludeIndex)
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7 from boto.dynamodb2.items import Item
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8 from boto.dynamodb2.layer1 import DynamoDBConnection
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9 from boto.dynamodb2.results import ResultSet, BatchGetResultSet
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10 from boto.dynamodb2.types import (NonBooleanDynamizer, Dynamizer, FILTER_OPERATORS,
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11 QUERY_OPERATORS, STRING)
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12 from boto.exception import JSONResponseError
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13
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14
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15 class Table(object):
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16 """
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17 Interacts & models the behavior of a DynamoDB table.
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18
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19 The ``Table`` object represents a set (or rough categorization) of
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20 records within DynamoDB. The important part is that all records within the
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21 table, while largely-schema-free, share the same schema & are essentially
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22 namespaced for use in your application. For example, you might have a
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23 ``users`` table or a ``forums`` table.
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24 """
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25 max_batch_get = 100
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26
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27 _PROJECTION_TYPE_TO_INDEX = dict(
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28 global_indexes=dict(
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29 ALL=GlobalAllIndex,
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30 KEYS_ONLY=GlobalKeysOnlyIndex,
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31 INCLUDE=GlobalIncludeIndex,
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32 ), local_indexes=dict(
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33 ALL=AllIndex,
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34 KEYS_ONLY=KeysOnlyIndex,
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35 INCLUDE=IncludeIndex,
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36 )
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37 )
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38
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39 def __init__(self, table_name, schema=None, throughput=None, indexes=None,
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40 global_indexes=None, connection=None):
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41 """
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42 Sets up a new in-memory ``Table``.
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43
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44 This is useful if the table already exists within DynamoDB & you simply
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45 want to use it for additional interactions. The only required parameter
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46 is the ``table_name``. However, under the hood, the object will call
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47 ``describe_table`` to determine the schema/indexes/throughput. You
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48 can avoid this extra call by passing in ``schema`` & ``indexes``.
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49
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50 **IMPORTANT** - If you're creating a new ``Table`` for the first time,
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51 you should use the ``Table.create`` method instead, as it will
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52 persist the table structure to DynamoDB.
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53
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54 Requires a ``table_name`` parameter, which should be a simple string
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55 of the name of the table.
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56
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57 Optionally accepts a ``schema`` parameter, which should be a list of
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58 ``BaseSchemaField`` subclasses representing the desired schema.
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59
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60 Optionally accepts a ``throughput`` parameter, which should be a
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61 dictionary. If provided, it should specify a ``read`` & ``write`` key,
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62 both of which should have an integer value associated with them.
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63
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64 Optionally accepts a ``indexes`` parameter, which should be a list of
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65 ``BaseIndexField`` subclasses representing the desired indexes.
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66
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67 Optionally accepts a ``global_indexes`` parameter, which should be a
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68 list of ``GlobalBaseIndexField`` subclasses representing the desired
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69 indexes.
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70
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71 Optionally accepts a ``connection`` parameter, which should be a
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72 ``DynamoDBConnection`` instance (or subclass). This is primarily useful
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73 for specifying alternate connection parameters.
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74
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75 Example::
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76
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77 # The simple, it-already-exists case.
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78 >>> conn = Table('users')
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79
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80 # The full, minimum-extra-calls case.
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81 >>> from boto import dynamodb2
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82 >>> users = Table('users', schema=[
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83 ... HashKey('username'),
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84 ... RangeKey('date_joined', data_type=NUMBER)
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85 ... ], throughput={
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86 ... 'read':20,
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87 ... 'write': 10,
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88 ... }, indexes=[
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89 ... KeysOnlyIndex('MostRecentlyJoined', parts=[
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90 ... HashKey('username')
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91 ... RangeKey('date_joined')
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92 ... ]),
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93 ... ], global_indexes=[
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94 ... GlobalAllIndex('UsersByZipcode', parts=[
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95 ... HashKey('zipcode'),
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96 ... RangeKey('username'),
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97 ... ],
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98 ... throughput={
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99 ... 'read':10,
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100 ... 'write":10,
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101 ... }),
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102 ... ], connection=dynamodb2.connect_to_region('us-west-2',
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103 ... aws_access_key_id='key',
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104 ... aws_secret_access_key='key',
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105 ... ))
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106
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107 """
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108 self.table_name = table_name
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109 self.connection = connection
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110 self.throughput = {
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111 'read': 5,
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112 'write': 5,
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113 }
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114 self.schema = schema
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115 self.indexes = indexes
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116 self.global_indexes = global_indexes
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117
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118 if self.connection is None:
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119 self.connection = DynamoDBConnection()
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120
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121 if throughput is not None:
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122 self.throughput = throughput
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123
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124 self._dynamizer = NonBooleanDynamizer()
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125
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126 def use_boolean(self):
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127 self._dynamizer = Dynamizer()
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128
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129 @classmethod
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130 def create(cls, table_name, schema, throughput=None, indexes=None,
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131 global_indexes=None, connection=None):
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132 """
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133 Creates a new table in DynamoDB & returns an in-memory ``Table`` object.
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134
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135 This will setup a brand new table within DynamoDB. The ``table_name``
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136 must be unique for your AWS account. The ``schema`` is also required
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137 to define the key structure of the table.
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138
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139 **IMPORTANT** - You should consider the usage pattern of your table
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140 up-front, as the schema can **NOT** be modified once the table is
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141 created, requiring the creation of a new table & migrating the data
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142 should you wish to revise it.
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143
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144 **IMPORTANT** - If the table already exists in DynamoDB, additional
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145 calls to this method will result in an error. If you just need
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146 a ``Table`` object to interact with the existing table, you should
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147 just initialize a new ``Table`` object, which requires only the
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148 ``table_name``.
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149
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150 Requires a ``table_name`` parameter, which should be a simple string
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151 of the name of the table.
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152
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153 Requires a ``schema`` parameter, which should be a list of
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154 ``BaseSchemaField`` subclasses representing the desired schema.
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155
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156 Optionally accepts a ``throughput`` parameter, which should be a
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157 dictionary. If provided, it should specify a ``read`` & ``write`` key,
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158 both of which should have an integer value associated with them.
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159
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160 Optionally accepts a ``indexes`` parameter, which should be a list of
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161 ``BaseIndexField`` subclasses representing the desired indexes.
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162
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163 Optionally accepts a ``global_indexes`` parameter, which should be a
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164 list of ``GlobalBaseIndexField`` subclasses representing the desired
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165 indexes.
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166
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167 Optionally accepts a ``connection`` parameter, which should be a
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168 ``DynamoDBConnection`` instance (or subclass). This is primarily useful
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169 for specifying alternate connection parameters.
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170
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171 Example::
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172
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173 >>> users = Table.create('users', schema=[
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174 ... HashKey('username'),
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175 ... RangeKey('date_joined', data_type=NUMBER)
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176 ... ], throughput={
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177 ... 'read':20,
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178 ... 'write': 10,
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179 ... }, indexes=[
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180 ... KeysOnlyIndex('MostRecentlyJoined', parts=[
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181 ... RangeKey('date_joined')
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182 ... ]), global_indexes=[
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183 ... GlobalAllIndex('UsersByZipcode', parts=[
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184 ... HashKey('zipcode'),
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185 ... RangeKey('username'),
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186 ... ],
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187 ... throughput={
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188 ... 'read':10,
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189 ... 'write':10,
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190 ... }),
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191 ... ])
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192
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193 """
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194 table = cls(table_name=table_name, connection=connection)
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195 table.schema = schema
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196
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197 if throughput is not None:
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198 table.throughput = throughput
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199
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200 if indexes is not None:
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201 table.indexes = indexes
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202
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203 if global_indexes is not None:
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204 table.global_indexes = global_indexes
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205
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206 # Prep the schema.
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207 raw_schema = []
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208 attr_defs = []
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209 seen_attrs = set()
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210
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211 for field in table.schema:
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212 raw_schema.append(field.schema())
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213 # Build the attributes off what we know.
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214 seen_attrs.add(field.name)
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215 attr_defs.append(field.definition())
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216
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217 raw_throughput = {
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218 'ReadCapacityUnits': int(table.throughput['read']),
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219 'WriteCapacityUnits': int(table.throughput['write']),
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220 }
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221 kwargs = {}
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222
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223 kwarg_map = {
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224 'indexes': 'local_secondary_indexes',
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225 'global_indexes': 'global_secondary_indexes',
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226 }
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227 for index_attr in ('indexes', 'global_indexes'):
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228 table_indexes = getattr(table, index_attr)
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229 if table_indexes:
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230 raw_indexes = []
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231 for index_field in table_indexes:
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232 raw_indexes.append(index_field.schema())
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233 # Make sure all attributes specified in the indexes are
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234 # added to the definition
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235 for field in index_field.parts:
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236 if field.name not in seen_attrs:
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237 seen_attrs.add(field.name)
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238 attr_defs.append(field.definition())
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239
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240 kwargs[kwarg_map[index_attr]] = raw_indexes
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241
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242 table.connection.create_table(
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243 table_name=table.table_name,
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244 attribute_definitions=attr_defs,
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245 key_schema=raw_schema,
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246 provisioned_throughput=raw_throughput,
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247 **kwargs
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248 )
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249 return table
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250
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251 def _introspect_schema(self, raw_schema, raw_attributes=None):
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252 """
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253 Given a raw schema structure back from a DynamoDB response, parse
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254 out & build the high-level Python objects that represent them.
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255 """
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256 schema = []
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257 sane_attributes = {}
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258
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259 if raw_attributes:
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260 for field in raw_attributes:
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261 sane_attributes[field['AttributeName']] = field['AttributeType']
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262
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263 for field in raw_schema:
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264 data_type = sane_attributes.get(field['AttributeName'], STRING)
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265
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266 if field['KeyType'] == 'HASH':
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267 schema.append(
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268 HashKey(field['AttributeName'], data_type=data_type)
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269 )
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270 elif field['KeyType'] == 'RANGE':
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271 schema.append(
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272 RangeKey(field['AttributeName'], data_type=data_type)
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273 )
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274 else:
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275 raise exceptions.UnknownSchemaFieldError(
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276 "%s was seen, but is unknown. Please report this at "
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277 "https://github.com/boto/boto/issues." % field['KeyType']
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278 )
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279
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280 return schema
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281
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282 def _introspect_all_indexes(self, raw_indexes, map_indexes_projection):
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283 """
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284 Given a raw index/global index structure back from a DynamoDB response,
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285 parse out & build the high-level Python objects that represent them.
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286 """
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287 indexes = []
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288
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289 for field in raw_indexes:
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290 index_klass = map_indexes_projection.get('ALL')
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291 kwargs = {
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292 'parts': []
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293 }
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294
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295 if field['Projection']['ProjectionType'] == 'ALL':
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296 index_klass = map_indexes_projection.get('ALL')
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297 elif field['Projection']['ProjectionType'] == 'KEYS_ONLY':
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298 index_klass = map_indexes_projection.get('KEYS_ONLY')
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299 elif field['Projection']['ProjectionType'] == 'INCLUDE':
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300 index_klass = map_indexes_projection.get('INCLUDE')
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301 kwargs['includes'] = field['Projection']['NonKeyAttributes']
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302 else:
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303 raise exceptions.UnknownIndexFieldError(
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304 "%s was seen, but is unknown. Please report this at "
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305 "https://github.com/boto/boto/issues." % \
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306 field['Projection']['ProjectionType']
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307 )
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308
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309 name = field['IndexName']
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310 kwargs['parts'] = self._introspect_schema(field['KeySchema'], None)
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311 indexes.append(index_klass(name, **kwargs))
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312
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313 return indexes
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314
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315 def _introspect_indexes(self, raw_indexes):
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316 """
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317 Given a raw index structure back from a DynamoDB response, parse
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318 out & build the high-level Python objects that represent them.
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319 """
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320 return self._introspect_all_indexes(
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321 raw_indexes, self._PROJECTION_TYPE_TO_INDEX.get('local_indexes'))
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322
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323 def _introspect_global_indexes(self, raw_global_indexes):
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324 """
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325 Given a raw global index structure back from a DynamoDB response, parse
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326 out & build the high-level Python objects that represent them.
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327 """
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328 return self._introspect_all_indexes(
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329 raw_global_indexes,
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330 self._PROJECTION_TYPE_TO_INDEX.get('global_indexes'))
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331
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332 def describe(self):
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333 """
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334 Describes the current structure of the table in DynamoDB.
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335
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336 This information will be used to update the ``schema``, ``indexes``,
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337 ``global_indexes`` and ``throughput`` information on the ``Table``. Some
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338 calls, such as those involving creating keys or querying, will require
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339 this information to be populated.
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340
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341 It also returns the full raw data structure from DynamoDB, in the
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342 event you'd like to parse out additional information (such as the
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343 ``ItemCount`` or usage information).
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344
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345 Example::
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346
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347 >>> users.describe()
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348 {
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349 # Lots of keys here...
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350 }
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351 >>> len(users.schema)
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352 2
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353
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354 """
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355 result = self.connection.describe_table(self.table_name)
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356
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357 # Blindly update throughput, since what's on DynamoDB's end is likely
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358 # more correct.
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359 raw_throughput = result['Table']['ProvisionedThroughput']
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360 self.throughput['read'] = int(raw_throughput['ReadCapacityUnits'])
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361 self.throughput['write'] = int(raw_throughput['WriteCapacityUnits'])
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362
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363 if not self.schema:
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364 # Since we have the data, build the schema.
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365 raw_schema = result['Table'].get('KeySchema', [])
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366 raw_attributes = result['Table'].get('AttributeDefinitions', [])
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367 self.schema = self._introspect_schema(raw_schema, raw_attributes)
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368
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369 if not self.indexes:
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370 # Build the index information as well.
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371 raw_indexes = result['Table'].get('LocalSecondaryIndexes', [])
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372 self.indexes = self._introspect_indexes(raw_indexes)
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373
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374 # Build the global index information as well.
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375 raw_global_indexes = result['Table'].get('GlobalSecondaryIndexes', [])
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376 self.global_indexes = self._introspect_global_indexes(raw_global_indexes)
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377
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378 # This is leaky.
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379 return result
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380
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381 def update(self, throughput=None, global_indexes=None):
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382 """
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383 Updates table attributes and global indexes in DynamoDB.
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384
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385 Optionally accepts a ``throughput`` parameter, which should be a
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386 dictionary. If provided, it should specify a ``read`` & ``write`` key,
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387 both of which should have an integer value associated with them.
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388
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389 Optionally accepts a ``global_indexes`` parameter, which should be a
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390 dictionary. If provided, it should specify the index name, which is also
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391 a dict containing a ``read`` & ``write`` key, both of which
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392 should have an integer value associated with them. If you are writing
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393 new code, please use ``Table.update_global_secondary_index``.
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394
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395 Returns ``True`` on success.
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396
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397 Example::
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398
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399 # For a read-heavier application...
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400 >>> users.update(throughput={
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401 ... 'read': 20,
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402 ... 'write': 10,
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403 ... })
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404 True
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405
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406 # To also update the global index(es) throughput.
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407 >>> users.update(throughput={
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408 ... 'read': 20,
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409 ... 'write': 10,
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410 ... },
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411 ... global_secondary_indexes={
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412 ... 'TheIndexNameHere': {
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413 ... 'read': 15,
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414 ... 'write': 5,
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415 ... }
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416 ... })
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417 True
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418 """
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419
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420 data = None
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421
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422 if throughput:
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423 self.throughput = throughput
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424 data = {
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425 'ReadCapacityUnits': int(self.throughput['read']),
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426 'WriteCapacityUnits': int(self.throughput['write']),
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427 }
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428
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429 gsi_data = None
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430
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431 if global_indexes:
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432 gsi_data = []
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433
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434 for gsi_name, gsi_throughput in global_indexes.items():
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435 gsi_data.append({
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436 "Update": {
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437 "IndexName": gsi_name,
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438 "ProvisionedThroughput": {
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439 "ReadCapacityUnits": int(gsi_throughput['read']),
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440 "WriteCapacityUnits": int(gsi_throughput['write']),
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441 },
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442 },
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443 })
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444
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445 if throughput or global_indexes:
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446 self.connection.update_table(
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447 self.table_name,
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448 provisioned_throughput=data,
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449 global_secondary_index_updates=gsi_data,
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450 )
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451
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452 return True
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453 else:
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454 msg = 'You need to provide either the throughput or the ' \
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455 'global_indexes to update method'
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456 boto.log.error(msg)
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457
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458 return False
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459
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460 def create_global_secondary_index(self, global_index):
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461 """
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462 Creates a global index in DynamoDB after the table has been created.
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463
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464 Requires a ``global_indexes`` parameter, which should be a
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465 ``GlobalBaseIndexField`` subclass representing the desired index.
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466
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467 To update ``global_indexes`` information on the ``Table``, you'll need
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468 to call ``Table.describe``.
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469
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470 Returns ``True`` on success.
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471
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472 Example::
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473
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474 # To create a global index
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475 >>> users.create_global_secondary_index(
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476 ... global_index=GlobalAllIndex(
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477 ... 'TheIndexNameHere', parts=[
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478 ... HashKey('requiredHashkey', data_type=STRING),
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479 ... RangeKey('optionalRangeKey', data_type=STRING)
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480 ... ],
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481 ... throughput={
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482 ... 'read': 2,
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483 ... 'write': 1,
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484 ... })
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485 ... )
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486 True
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487
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488 """
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489
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490 if global_index:
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491 gsi_data = []
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492 gsi_data_attr_def = []
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493
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494 gsi_data.append({
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495 "Create": global_index.schema()
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496 })
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497
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498 for attr_def in global_index.parts:
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499 gsi_data_attr_def.append(attr_def.definition())
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500
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501 self.connection.update_table(
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502 self.table_name,
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503 global_secondary_index_updates=gsi_data,
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504 attribute_definitions=gsi_data_attr_def
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505 )
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506
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507 return True
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508 else:
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509 msg = 'You need to provide the global_index to ' \
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510 'create_global_secondary_index method'
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511 boto.log.error(msg)
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512
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513 return False
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514
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515 def delete_global_secondary_index(self, global_index_name):
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516 """
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517 Deletes a global index in DynamoDB after the table has been created.
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518
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519 Requires a ``global_index_name`` parameter, which should be a simple
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520 string of the name of the global secondary index.
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521
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522 To update ``global_indexes`` information on the ``Table``, you'll need
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523 to call ``Table.describe``.
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524
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525 Returns ``True`` on success.
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526
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527 Example::
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528
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529 # To delete a global index
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530 >>> users.delete_global_secondary_index('TheIndexNameHere')
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531 True
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532
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533 """
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534
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535 if global_index_name:
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536 gsi_data = [
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537 {
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538 "Delete": {
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539 "IndexName": global_index_name
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540 }
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541 }
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542 ]
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543
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544 self.connection.update_table(
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545 self.table_name,
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546 global_secondary_index_updates=gsi_data,
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547 )
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548
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549 return True
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550 else:
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551 msg = 'You need to provide the global index name to ' \
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552 'delete_global_secondary_index method'
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553 boto.log.error(msg)
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554
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555 return False
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556
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557 def update_global_secondary_index(self, global_indexes):
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558 """
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559 Updates a global index(es) in DynamoDB after the table has been created.
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560
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561 Requires a ``global_indexes`` parameter, which should be a
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562 dictionary. If provided, it should specify the index name, which is also
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563 a dict containing a ``read`` & ``write`` key, both of which
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564 should have an integer value associated with them.
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565
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566 To update ``global_indexes`` information on the ``Table``, you'll need
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567 to call ``Table.describe``.
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568
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569 Returns ``True`` on success.
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570
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571 Example::
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572
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573 # To update a global index
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574 >>> users.update_global_secondary_index(global_indexes={
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575 ... 'TheIndexNameHere': {
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576 ... 'read': 15,
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577 ... 'write': 5,
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578 ... }
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579 ... })
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580 True
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581
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582 """
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583
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584 if global_indexes:
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585 gsi_data = []
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586
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587 for gsi_name, gsi_throughput in global_indexes.items():
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588 gsi_data.append({
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589 "Update": {
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590 "IndexName": gsi_name,
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591 "ProvisionedThroughput": {
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592 "ReadCapacityUnits": int(gsi_throughput['read']),
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593 "WriteCapacityUnits": int(gsi_throughput['write']),
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594 },
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595 },
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596 })
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597
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598 self.connection.update_table(
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599 self.table_name,
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600 global_secondary_index_updates=gsi_data,
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601 )
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602 return True
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603 else:
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604 msg = 'You need to provide the global indexes to ' \
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605 'update_global_secondary_index method'
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606 boto.log.error(msg)
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607
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608 return False
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609
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610 def delete(self):
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611 """
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612 Deletes a table in DynamoDB.
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613
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614 **IMPORTANT** - Be careful when using this method, there is no undo.
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615
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616 Returns ``True`` on success.
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617
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618 Example::
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619
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620 >>> users.delete()
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621 True
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622
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623 """
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624 self.connection.delete_table(self.table_name)
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625 return True
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626
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627 def _encode_keys(self, keys):
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628 """
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629 Given a flat Python dictionary of keys/values, converts it into the
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630 nested dictionary DynamoDB expects.
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631
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632 Converts::
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633
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634 {
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635 'username': 'john',
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636 'tags': [1, 2, 5],
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637 }
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638
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639 ...to...::
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640
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641 {
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642 'username': {'S': 'john'},
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643 'tags': {'NS': ['1', '2', '5']},
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644 }
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645
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646 """
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647 raw_key = {}
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648
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649 for key, value in keys.items():
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650 raw_key[key] = self._dynamizer.encode(value)
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651
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652 return raw_key
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653
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654 def get_item(self, consistent=False, attributes=None, **kwargs):
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655 """
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656 Fetches an item (record) from a table in DynamoDB.
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657
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658 To specify the key of the item you'd like to get, you can specify the
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659 key attributes as kwargs.
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660
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661 Optionally accepts a ``consistent`` parameter, which should be a
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662 boolean. If you provide ``True``, it will perform
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663 a consistent (but more expensive) read from DynamoDB.
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664 (Default: ``False``)
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665
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666 Optionally accepts an ``attributes`` parameter, which should be a
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667 list of fieldname to fetch. (Default: ``None``, which means all fields
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668 should be fetched)
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669
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670 Returns an ``Item`` instance containing all the data for that record.
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671
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672 Raises an ``ItemNotFound`` exception if the item is not found.
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673
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674 Example::
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675
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676 # A simple hash key.
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677 >>> john = users.get_item(username='johndoe')
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678 >>> john['first_name']
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679 'John'
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680
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681 # A complex hash+range key.
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682 >>> john = users.get_item(username='johndoe', last_name='Doe')
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683 >>> john['first_name']
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684 'John'
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685
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686 # A consistent read (assuming the data might have just changed).
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687 >>> john = users.get_item(username='johndoe', consistent=True)
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688 >>> john['first_name']
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689 'Johann'
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690
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691 # With a key that is an invalid variable name in Python.
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692 # Also, assumes a different schema than previous examples.
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693 >>> john = users.get_item(**{
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694 ... 'date-joined': 127549192,
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695 ... })
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696 >>> john['first_name']
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697 'John'
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698
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699 """
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700 raw_key = self._encode_keys(kwargs)
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701 item_data = self.connection.get_item(
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702 self.table_name,
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703 raw_key,
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704 attributes_to_get=attributes,
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705 consistent_read=consistent
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706 )
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707 if 'Item' not in item_data:
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708 raise exceptions.ItemNotFound("Item %s couldn't be found." % kwargs)
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709 item = Item(self)
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710 item.load(item_data)
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711 return item
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712
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713 def has_item(self, **kwargs):
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714 """
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715 Return whether an item (record) exists within a table in DynamoDB.
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716
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717 To specify the key of the item you'd like to get, you can specify the
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718 key attributes as kwargs.
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719
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720 Optionally accepts a ``consistent`` parameter, which should be a
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721 boolean. If you provide ``True``, it will perform
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722 a consistent (but more expensive) read from DynamoDB.
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723 (Default: ``False``)
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724
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725 Optionally accepts an ``attributes`` parameter, which should be a
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726 list of fieldnames to fetch. (Default: ``None``, which means all fields
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diff changeset
727 should be fetched)
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728
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729 Returns ``True`` if an ``Item`` is present, ``False`` if not.
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730
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731 Example::
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732
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733 # Simple, just hash-key schema.
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734 >>> users.has_item(username='johndoe')
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735 True
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736
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737 # Complex schema, item not present.
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738 >>> users.has_item(
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739 ... username='johndoe',
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740 ... date_joined='2014-01-07'
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741 ... )
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742 False
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743
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744 """
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745 try:
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746 self.get_item(**kwargs)
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747 except (JSONResponseError, exceptions.ItemNotFound):
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748 return False
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749
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750 return True
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751
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752 def lookup(self, *args, **kwargs):
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753 """
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754 Look up an entry in DynamoDB. This is mostly backwards compatible
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755 with boto.dynamodb. Unlike get_item, it takes hash_key and range_key first,
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756 although you may still specify keyword arguments instead.
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757
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758 Also unlike the get_item command, if the returned item has no keys
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759 (i.e., it does not exist in DynamoDB), a None result is returned, instead
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760 of an empty key object.
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761
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762 Example::
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763 >>> user = users.lookup(username)
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764 >>> user = users.lookup(username, consistent=True)
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765 >>> app = apps.lookup('my_customer_id', 'my_app_id')
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766
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767 """
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768 if not self.schema:
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769 self.describe()
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770 for x, arg in enumerate(args):
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771 kwargs[self.schema[x].name] = arg
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772 ret = self.get_item(**kwargs)
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773 if not ret.keys():
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774 return None
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775 return ret
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776
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777 def new_item(self, *args):
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778 """
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779 Returns a new, blank item
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780
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781 This is mostly for consistency with boto.dynamodb
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782 """
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783 if not self.schema:
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784 self.describe()
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785 data = {}
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786 for x, arg in enumerate(args):
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787 data[self.schema[x].name] = arg
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788 return Item(self, data=data)
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789
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790 def put_item(self, data, overwrite=False):
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791 """
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792 Saves an entire item to DynamoDB.
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793
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794 By default, if any part of the ``Item``'s original data doesn't match
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795 what's currently in DynamoDB, this request will fail. This prevents
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796 other processes from updating the data in between when you read the
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797 item & when your request to update the item's data is processed, which
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798 would typically result in some data loss.
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799
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800 Requires a ``data`` parameter, which should be a dictionary of the data
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801 you'd like to store in DynamoDB.
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802
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803 Optionally accepts an ``overwrite`` parameter, which should be a
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804 boolean. If you provide ``True``, this will tell DynamoDB to blindly
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805 overwrite whatever data is present, if any.
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806
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807 Returns ``True`` on success.
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808
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809 Example::
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810
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811 >>> users.put_item(data={
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812 ... 'username': 'jane',
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813 ... 'first_name': 'Jane',
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814 ... 'last_name': 'Doe',
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815 ... 'date_joined': 126478915,
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816 ... })
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817 True
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818
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819 """
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820 item = Item(self, data=data)
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821 return item.save(overwrite=overwrite)
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822
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823 def _put_item(self, item_data, expects=None):
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824 """
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825 The internal variant of ``put_item`` (full data). This is used by the
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826 ``Item`` objects, since that operation is represented at the
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827 table-level by the API, but conceptually maps better to telling an
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828 individual ``Item`` to save itself.
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829 """
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830 kwargs = {}
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831
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832 if expects is not None:
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833 kwargs['expected'] = expects
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834
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835 self.connection.put_item(self.table_name, item_data, **kwargs)
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836 return True
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837
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838 def _update_item(self, key, item_data, expects=None):
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839 """
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840 The internal variant of ``put_item`` (partial data). This is used by the
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841 ``Item`` objects, since that operation is represented at the
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842 table-level by the API, but conceptually maps better to telling an
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843 individual ``Item`` to save itself.
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844 """
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845 raw_key = self._encode_keys(key)
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846 kwargs = {}
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847
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848 if expects is not None:
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849 kwargs['expected'] = expects
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850
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851 self.connection.update_item(self.table_name, raw_key, item_data, **kwargs)
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852 return True
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853
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854 def delete_item(self, expected=None, conditional_operator=None, **kwargs):
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855 """
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856 Deletes a single item. You can perform a conditional delete operation
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857 that deletes the item if it exists, or if it has an expected attribute
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858 value.
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859
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860 Conditional deletes are useful for only deleting items if specific
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861 conditions are met. If those conditions are met, DynamoDB performs
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862 the delete. Otherwise, the item is not deleted.
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863
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864 To specify the expected attribute values of the item, you can pass a
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865 dictionary of conditions to ``expected``. Each condition should follow
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866 the pattern ``<attributename>__<comparison_operator>=<value_to_expect>``.
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867
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868 **IMPORTANT** - Be careful when using this method, there is no undo.
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869
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870 To specify the key of the item you'd like to get, you can specify the
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871 key attributes as kwargs.
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872
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873 Optionally accepts an ``expected`` parameter which is a dictionary of
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874 expected attribute value conditions.
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875
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876 Optionally accepts a ``conditional_operator`` which applies to the
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877 expected attribute value conditions:
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878
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879 + `AND` - If all of the conditions evaluate to true (default)
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880 + `OR` - True if at least one condition evaluates to true
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881
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882 Returns ``True`` on success, ``False`` on failed conditional delete.
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883
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884 Example::
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885
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886 # A simple hash key.
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887 >>> users.delete_item(username='johndoe')
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888 True
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889
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890 # A complex hash+range key.
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891 >>> users.delete_item(username='jane', last_name='Doe')
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892 True
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893
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894 # With a key that is an invalid variable name in Python.
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895 # Also, assumes a different schema than previous examples.
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896 >>> users.delete_item(**{
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897 ... 'date-joined': 127549192,
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898 ... })
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899 True
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900
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901 # Conditional delete
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902 >>> users.delete_item(username='johndoe',
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parents:
diff changeset
903 ... expected={'balance__eq': 0})
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diff changeset
904 True
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parents:
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905 """
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parents:
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906 expected = self._build_filters(expected, using=FILTER_OPERATORS)
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907 raw_key = self._encode_keys(kwargs)
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diff changeset
908
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diff changeset
909 try:
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910 self.connection.delete_item(self.table_name, raw_key,
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diff changeset
911 expected=expected,
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diff changeset
912 conditional_operator=conditional_operator)
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diff changeset
913 except exceptions.ConditionalCheckFailedException:
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parents:
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914 return False
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parents:
diff changeset
915
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parents:
diff changeset
916 return True
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917
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918 def get_key_fields(self):
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parents:
diff changeset
919 """
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920 Returns the fields necessary to make a key for a table.
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diff changeset
921
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922 If the ``Table`` does not already have a populated ``schema``,
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923 this will request it via a ``Table.describe`` call.
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924
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925 Returns a list of fieldnames (strings).
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926
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927 Example::
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928
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929 # A simple hash key.
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930 >>> users.get_key_fields()
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931 ['username']
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932
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933 # A complex hash+range key.
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934 >>> users.get_key_fields()
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935 ['username', 'last_name']
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936
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937 """
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938 if not self.schema:
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939 # We don't know the structure of the table. Get a description to
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940 # populate the schema.
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941 self.describe()
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942
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943 return [field.name for field in self.schema]
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944
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945 def batch_write(self):
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946 """
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947 Allows the batching of writes to DynamoDB.
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948
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949 Since each write/delete call to DynamoDB has a cost associated with it,
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950 when loading lots of data, it makes sense to batch them, creating as
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951 few calls as possible.
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952
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953 This returns a context manager that will transparently handle creating
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954 these batches. The object you get back lightly-resembles a ``Table``
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955 object, sharing just the ``put_item`` & ``delete_item`` methods
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956 (which are all that DynamoDB can batch in terms of writing data).
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957
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958 DynamoDB's maximum batch size is 25 items per request. If you attempt
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959 to put/delete more than that, the context manager will batch as many
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960 as it can up to that number, then flush them to DynamoDB & continue
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961 batching as more calls come in.
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962
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963 Example::
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964
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965 # Assuming a table with one record...
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parents:
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966 >>> with users.batch_write() as batch:
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967 ... batch.put_item(data={
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968 ... 'username': 'johndoe',
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969 ... 'first_name': 'John',
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diff changeset
970 ... 'last_name': 'Doe',
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parents:
diff changeset
971 ... 'owner': 1,
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parents:
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972 ... })
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973 ... # Nothing across the wire yet.
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974 ... batch.delete_item(username='bob')
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parents:
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975 ... # Still no requests sent.
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976 ... batch.put_item(data={
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977 ... 'username': 'jane',
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parents:
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978 ... 'first_name': 'Jane',
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diff changeset
979 ... 'last_name': 'Doe',
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diff changeset
980 ... 'date_joined': 127436192,
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981 ... })
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982 ... # Nothing yet, but once we leave the context, the
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983 ... # put/deletes will be sent.
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984
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985 """
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986 # PHENOMENAL COSMIC DOCS!!! itty-bitty code.
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987 return BatchTable(self)
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988
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989 def _build_filters(self, filter_kwargs, using=QUERY_OPERATORS):
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990 """
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991 An internal method for taking query/scan-style ``**kwargs`` & turning
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992 them into the raw structure DynamoDB expects for filtering.
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993 """
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994 if filter_kwargs is None:
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995 return
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996
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997 filters = {}
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998
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999 for field_and_op, value in filter_kwargs.items():
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1000 field_bits = field_and_op.split('__')
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1001 fieldname = '__'.join(field_bits[:-1])
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1002
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1003 try:
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1004 op = using[field_bits[-1]]
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1005 except KeyError:
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1006 raise exceptions.UnknownFilterTypeError(
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1007 "Operator '%s' from '%s' is not recognized." % (
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1008 field_bits[-1],
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1009 field_and_op
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1010 )
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1011 )
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1012
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1013 lookup = {
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1014 'AttributeValueList': [],
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1015 'ComparisonOperator': op,
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1016 }
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1017
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1018 # Special-case the ``NULL/NOT_NULL`` case.
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1019 if field_bits[-1] == 'null':
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1020 del lookup['AttributeValueList']
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1021
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1022 if value is False:
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1023 lookup['ComparisonOperator'] = 'NOT_NULL'
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1024 else:
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1025 lookup['ComparisonOperator'] = 'NULL'
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1026 # Special-case the ``BETWEEN`` case.
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1027 elif field_bits[-1] == 'between':
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1028 if len(value) == 2 and isinstance(value, (list, tuple)):
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1029 lookup['AttributeValueList'].append(
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1030 self._dynamizer.encode(value[0])
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1031 )
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1032 lookup['AttributeValueList'].append(
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1033 self._dynamizer.encode(value[1])
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1034 )
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1035 # Special-case the ``IN`` case
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1036 elif field_bits[-1] == 'in':
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1037 for val in value:
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1038 lookup['AttributeValueList'].append(self._dynamizer.encode(val))
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1039 else:
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1040 # Fix up the value for encoding, because it was built to only work
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1041 # with ``set``s.
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1042 if isinstance(value, (list, tuple)):
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1043 value = set(value)
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1044 lookup['AttributeValueList'].append(
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1045 self._dynamizer.encode(value)
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1046 )
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1047
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1048 # Finally, insert it into the filters.
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1049 filters[fieldname] = lookup
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1050
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1051 return filters
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1052
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1053 def query(self, limit=None, index=None, reverse=False, consistent=False,
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1054 attributes=None, max_page_size=None, **filter_kwargs):
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1055 """
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1056 **WARNING:** This method is provided **strictly** for
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1057 backward-compatibility. It returns results in an incorrect order.
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1058
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1059 If you are writing new code, please use ``Table.query_2``.
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1060 """
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1061 reverse = not reverse
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1062 return self.query_2(limit=limit, index=index, reverse=reverse,
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1063 consistent=consistent, attributes=attributes,
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1064 max_page_size=max_page_size, **filter_kwargs)
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1065
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1066 def query_2(self, limit=None, index=None, reverse=False,
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1067 consistent=False, attributes=None, max_page_size=None,
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1068 query_filter=None, conditional_operator=None,
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1069 **filter_kwargs):
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1070 """
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1071 Queries for a set of matching items in a DynamoDB table.
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1072
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1073 Queries can be performed against a hash key, a hash+range key or
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1074 against any data stored in your local secondary indexes. Query filters
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1075 can be used to filter on arbitrary fields.
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1076
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1077 **Note** - You can not query against arbitrary fields within the data
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1078 stored in DynamoDB unless you specify ``query_filter`` values.
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1079
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1080 To specify the filters of the items you'd like to get, you can specify
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1081 the filters as kwargs. Each filter kwarg should follow the pattern
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1082 ``<fieldname>__<filter_operation>=<value_to_look_for>``. Query filters
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1083 are specified in the same way.
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1084
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1085 Optionally accepts a ``limit`` parameter, which should be an integer
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1086 count of the total number of items to return. (Default: ``None`` -
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1087 all results)
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1088
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1089 Optionally accepts an ``index`` parameter, which should be a string of
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1090 name of the local secondary index you want to query against.
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1091 (Default: ``None``)
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1092
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1093 Optionally accepts a ``reverse`` parameter, which will present the
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1094 results in reverse order. (Default: ``False`` - normal order)
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1095
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1096 Optionally accepts a ``consistent`` parameter, which should be a
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1097 boolean. If you provide ``True``, it will force a consistent read of
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1098 the data (more expensive). (Default: ``False`` - use eventually
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1099 consistent reads)
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1100
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1101 Optionally accepts a ``attributes`` parameter, which should be a
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1102 tuple. If you provide any attributes only these will be fetched
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1103 from DynamoDB. This uses the ``AttributesToGet`` and set's
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1104 ``Select`` to ``SPECIFIC_ATTRIBUTES`` API.
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1105
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1106 Optionally accepts a ``max_page_size`` parameter, which should be an
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1107 integer count of the maximum number of items to retrieve
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1108 **per-request**. This is useful in making faster requests & prevent
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1109 the scan from drowning out other queries. (Default: ``None`` -
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1110 fetch as many as DynamoDB will return)
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1111
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1112 Optionally accepts a ``query_filter`` which is a dictionary of filter
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1113 conditions against any arbitrary field in the returned data.
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1114
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1115 Optionally accepts a ``conditional_operator`` which applies to the
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1116 query filter conditions:
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1117
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1118 + `AND` - True if all filter conditions evaluate to true (default)
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1119 + `OR` - True if at least one filter condition evaluates to true
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1120
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1121 Returns a ``ResultSet``, which transparently handles the pagination of
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1122 results you get back.
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1123
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1124 Example::
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1125
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1126 # Look for last names equal to "Doe".
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1127 >>> results = users.query(last_name__eq='Doe')
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1128 >>> for res in results:
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1129 ... print res['first_name']
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1130 'John'
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1131 'Jane'
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1132
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1133 # Look for last names beginning with "D", in reverse order, limit 3.
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1134 >>> results = users.query(
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1135 ... last_name__beginswith='D',
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1136 ... reverse=True,
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1137 ... limit=3
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1138 ... )
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1139 >>> for res in results:
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1140 ... print res['first_name']
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1141 'Alice'
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1142 'Jane'
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1143 'John'
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1144
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1145 # Use an LSI & a consistent read.
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1146 >>> results = users.query(
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1147 ... date_joined__gte=1236451000,
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1148 ... owner__eq=1,
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1149 ... index='DateJoinedIndex',
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1150 ... consistent=True
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1151 ... )
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1152 >>> for res in results:
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1153 ... print res['first_name']
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1154 'Alice'
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1155 'Bob'
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1156 'John'
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1157 'Fred'
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1158
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1159 # Filter by non-indexed field(s)
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1160 >>> results = users.query(
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1161 ... last_name__eq='Doe',
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1162 ... reverse=True,
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1163 ... query_filter={
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1164 ... 'first_name__beginswith': 'A'
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1165 ... }
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1166 ... )
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1167 >>> for res in results:
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1168 ... print res['first_name'] + ' ' + res['last_name']
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1169 'Alice Doe'
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1170
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1171 """
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1172 if self.schema:
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1173 if len(self.schema) == 1:
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1174 if len(filter_kwargs) <= 1:
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1175 if not self.global_indexes or not len(self.global_indexes):
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1176 # If the schema only has one field, there's <= 1 filter
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1177 # param & no Global Secondary Indexes, this is user
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1178 # error. Bail early.
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1179 raise exceptions.QueryError(
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1180 "You must specify more than one key to filter on."
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1181 )
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1182
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1183 if attributes is not None:
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1184 select = 'SPECIFIC_ATTRIBUTES'
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1185 else:
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1186 select = None
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1187
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1188 results = ResultSet(
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1189 max_page_size=max_page_size
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1190 )
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1191 kwargs = filter_kwargs.copy()
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1192 kwargs.update({
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1193 'limit': limit,
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1194 'index': index,
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1195 'reverse': reverse,
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1196 'consistent': consistent,
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1197 'select': select,
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1198 'attributes_to_get': attributes,
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1199 'query_filter': query_filter,
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1200 'conditional_operator': conditional_operator,
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1201 })
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1202 results.to_call(self._query, **kwargs)
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1203 return results
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1204
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1205 def query_count(self, index=None, consistent=False, conditional_operator=None,
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1206 query_filter=None, scan_index_forward=True, limit=None,
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1207 exclusive_start_key=None, **filter_kwargs):
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1208 """
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1209 Queries the exact count of matching items in a DynamoDB table.
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1210
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1211 Queries can be performed against a hash key, a hash+range key or
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1212 against any data stored in your local secondary indexes. Query filters
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1213 can be used to filter on arbitrary fields.
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1214
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1215 To specify the filters of the items you'd like to get, you can specify
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1216 the filters as kwargs. Each filter kwarg should follow the pattern
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1217 ``<fieldname>__<filter_operation>=<value_to_look_for>``. Query filters
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1218 are specified in the same way.
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1219
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1220 Optionally accepts an ``index`` parameter, which should be a string of
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1221 name of the local secondary index you want to query against.
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1222 (Default: ``None``)
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1223
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1224 Optionally accepts a ``consistent`` parameter, which should be a
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1225 boolean. If you provide ``True``, it will force a consistent read of
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1226 the data (more expensive). (Default: ``False`` - use eventually
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1227 consistent reads)
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1228
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1229 Optionally accepts a ``query_filter`` which is a dictionary of filter
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1230 conditions against any arbitrary field in the returned data.
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1231
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1232 Optionally accepts a ``conditional_operator`` which applies to the
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1233 query filter conditions:
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1234
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1235 + `AND` - True if all filter conditions evaluate to true (default)
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1236 + `OR` - True if at least one filter condition evaluates to true
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1237
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1238 Optionally accept a ``exclusive_start_key`` which is used to get
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1239 the remaining items when a query cannot return the complete count.
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1240
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1241 Returns an integer which represents the exact amount of matched
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1242 items.
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1243
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1244 :type scan_index_forward: boolean
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1245 :param scan_index_forward: Specifies ascending (true) or descending
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1246 (false) traversal of the index. DynamoDB returns results reflecting
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1247 the requested order determined by the range key. If the data type
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1248 is Number, the results are returned in numeric order. For String,
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1249 the results are returned in order of ASCII character code values.
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1250 For Binary, DynamoDB treats each byte of the binary data as
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1251 unsigned when it compares binary values.
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1252
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1253 If ScanIndexForward is not specified, the results are returned in
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1254 ascending order.
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1255
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1256 :type limit: integer
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1257 :param limit: The maximum number of items to evaluate (not necessarily
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1258 the number of matching items).
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1259
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1260 Example::
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1261
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1262 # Look for last names equal to "Doe".
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1263 >>> users.query_count(last_name__eq='Doe')
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1264 5
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1265
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1266 # Use an LSI & a consistent read.
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1267 >>> users.query_count(
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1268 ... date_joined__gte=1236451000,
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1269 ... owner__eq=1,
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1270 ... index='DateJoinedIndex',
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1271 ... consistent=True
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1272 ... )
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1273 2
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1274
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1275 """
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1276 key_conditions = self._build_filters(
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1277 filter_kwargs,
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1278 using=QUERY_OPERATORS
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1279 )
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1280
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1281 built_query_filter = self._build_filters(
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1282 query_filter,
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1283 using=FILTER_OPERATORS
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1284 )
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1285
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1286 count_buffer = 0
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1287 last_evaluated_key = exclusive_start_key
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1288
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1289 while True:
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1290 raw_results = self.connection.query(
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1291 self.table_name,
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1292 index_name=index,
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1293 consistent_read=consistent,
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1294 select='COUNT',
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1295 key_conditions=key_conditions,
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1296 query_filter=built_query_filter,
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1297 conditional_operator=conditional_operator,
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1298 limit=limit,
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1299 scan_index_forward=scan_index_forward,
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1300 exclusive_start_key=last_evaluated_key
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1301 )
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1302
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1303 count_buffer += int(raw_results.get('Count', 0))
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1304 last_evaluated_key = raw_results.get('LastEvaluatedKey')
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1305 if not last_evaluated_key or count_buffer < 1:
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1306 break
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1307
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1308 return count_buffer
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1309
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1310 def _query(self, limit=None, index=None, reverse=False, consistent=False,
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1311 exclusive_start_key=None, select=None, attributes_to_get=None,
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1312 query_filter=None, conditional_operator=None, **filter_kwargs):
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1313 """
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1314 The internal method that performs the actual queries. Used extensively
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1315 by ``ResultSet`` to perform each (paginated) request.
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1316 """
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1317 kwargs = {
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1318 'limit': limit,
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1319 'index_name': index,
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1320 'consistent_read': consistent,
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1321 'select': select,
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1322 'attributes_to_get': attributes_to_get,
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1323 'conditional_operator': conditional_operator,
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1324 }
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1325
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1326 if reverse:
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1327 kwargs['scan_index_forward'] = False
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1328
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1329 if exclusive_start_key:
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1330 kwargs['exclusive_start_key'] = {}
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1331
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1332 for key, value in exclusive_start_key.items():
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1333 kwargs['exclusive_start_key'][key] = \
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1334 self._dynamizer.encode(value)
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1335
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1336 # Convert the filters into something we can actually use.
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1337 kwargs['key_conditions'] = self._build_filters(
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1338 filter_kwargs,
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1339 using=QUERY_OPERATORS
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1340 )
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1341
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1342 kwargs['query_filter'] = self._build_filters(
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1343 query_filter,
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1344 using=FILTER_OPERATORS
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1345 )
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1346
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1347 raw_results = self.connection.query(
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1348 self.table_name,
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1349 **kwargs
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1350 )
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1351 results = []
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1352 last_key = None
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1353
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1354 for raw_item in raw_results.get('Items', []):
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1355 item = Item(self)
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1356 item.load({
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1357 'Item': raw_item,
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1358 })
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1359 results.append(item)
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1360
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1361 if raw_results.get('LastEvaluatedKey', None):
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1362 last_key = {}
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1363
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1364 for key, value in raw_results['LastEvaluatedKey'].items():
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1365 last_key[key] = self._dynamizer.decode(value)
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1366
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1367 return {
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1368 'results': results,
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1369 'last_key': last_key,
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1370 }
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1371
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1372 def scan(self, limit=None, segment=None, total_segments=None,
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1373 max_page_size=None, attributes=None, conditional_operator=None,
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1374 **filter_kwargs):
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1375 """
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1376 Scans across all items within a DynamoDB table.
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1377
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1378 Scans can be performed against a hash key or a hash+range key. You can
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1379 additionally filter the results after the table has been read but
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1380 before the response is returned by using query filters.
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1381
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1382 To specify the filters of the items you'd like to get, you can specify
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1383 the filters as kwargs. Each filter kwarg should follow the pattern
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1384 ``<fieldname>__<filter_operation>=<value_to_look_for>``.
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1385
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1386 Optionally accepts a ``limit`` parameter, which should be an integer
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1387 count of the total number of items to return. (Default: ``None`` -
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1388 all results)
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1389
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1390 Optionally accepts a ``segment`` parameter, which should be an integer
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1391 of the segment to retrieve on. Please see the documentation about
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1392 Parallel Scans (Default: ``None`` - no segments)
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1393
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1394 Optionally accepts a ``total_segments`` parameter, which should be an
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1395 integer count of number of segments to divide the table into.
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1396 Please see the documentation about Parallel Scans (Default: ``None`` -
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1397 no segments)
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1398
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1399 Optionally accepts a ``max_page_size`` parameter, which should be an
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1400 integer count of the maximum number of items to retrieve
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1401 **per-request**. This is useful in making faster requests & prevent
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1402 the scan from drowning out other queries. (Default: ``None`` -
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1403 fetch as many as DynamoDB will return)
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1404
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1405 Optionally accepts an ``attributes`` parameter, which should be a
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1406 tuple. If you provide any attributes only these will be fetched
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1407 from DynamoDB. This uses the ``AttributesToGet`` and set's
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1408 ``Select`` to ``SPECIFIC_ATTRIBUTES`` API.
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1409
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1410 Returns a ``ResultSet``, which transparently handles the pagination of
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1411 results you get back.
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1412
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1413 Example::
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1414
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1415 # All results.
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1416 >>> everything = users.scan()
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1417
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1418 # Look for last names beginning with "D".
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1419 >>> results = users.scan(last_name__beginswith='D')
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1420 >>> for res in results:
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1421 ... print res['first_name']
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1422 'Alice'
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1423 'John'
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1424 'Jane'
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1425
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1426 # Use an ``IN`` filter & limit.
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1427 >>> results = users.scan(
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1428 ... age__in=[25, 26, 27, 28, 29],
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1429 ... limit=1
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1430 ... )
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1431 >>> for res in results:
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1432 ... print res['first_name']
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1433 'Alice'
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1434
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1435 """
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1436 results = ResultSet(
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1437 max_page_size=max_page_size
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1438 )
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1439 kwargs = filter_kwargs.copy()
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1440 kwargs.update({
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1441 'limit': limit,
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1442 'segment': segment,
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1443 'total_segments': total_segments,
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1444 'attributes': attributes,
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1445 'conditional_operator': conditional_operator,
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1446 })
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1447 results.to_call(self._scan, **kwargs)
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1448 return results
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1449
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1450 def _scan(self, limit=None, exclusive_start_key=None, segment=None,
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1451 total_segments=None, attributes=None, conditional_operator=None,
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1452 **filter_kwargs):
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1453 """
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1454 The internal method that performs the actual scan. Used extensively
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1455 by ``ResultSet`` to perform each (paginated) request.
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1456 """
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1457 kwargs = {
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1458 'limit': limit,
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1459 'segment': segment,
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1460 'total_segments': total_segments,
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1461 'attributes_to_get': attributes,
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1462 'conditional_operator': conditional_operator,
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1463 }
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1464
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1465 if exclusive_start_key:
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1466 kwargs['exclusive_start_key'] = {}
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1467
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1468 for key, value in exclusive_start_key.items():
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1469 kwargs['exclusive_start_key'][key] = \
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1470 self._dynamizer.encode(value)
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1471
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1472 # Convert the filters into something we can actually use.
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1473 kwargs['scan_filter'] = self._build_filters(
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1474 filter_kwargs,
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1475 using=FILTER_OPERATORS
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1476 )
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1477
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1478 raw_results = self.connection.scan(
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1479 self.table_name,
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1480 **kwargs
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1481 )
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1482 results = []
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1483 last_key = None
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1484
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1485 for raw_item in raw_results.get('Items', []):
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1486 item = Item(self)
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1487 item.load({
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1488 'Item': raw_item,
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1489 })
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1490 results.append(item)
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1491
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1492 if raw_results.get('LastEvaluatedKey', None):
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1493 last_key = {}
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1494
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1495 for key, value in raw_results['LastEvaluatedKey'].items():
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1496 last_key[key] = self._dynamizer.decode(value)
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1497
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1498 return {
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1499 'results': results,
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1500 'last_key': last_key,
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1501 }
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1502
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1503 def batch_get(self, keys, consistent=False, attributes=None):
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1504 """
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1505 Fetches many specific items in batch from a table.
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1506
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1507 Requires a ``keys`` parameter, which should be a list of dictionaries.
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1508 Each dictionary should consist of the keys values to specify.
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1509
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1510 Optionally accepts a ``consistent`` parameter, which should be a
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1511 boolean. If you provide ``True``, a strongly consistent read will be
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1512 used. (Default: False)
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1513
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1514 Optionally accepts an ``attributes`` parameter, which should be a
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1515 tuple. If you provide any attributes only these will be fetched
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1516 from DynamoDB.
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1517
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1518 Returns a ``ResultSet``, which transparently handles the pagination of
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1519 results you get back.
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1520
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1521 Example::
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1522
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1523 >>> results = users.batch_get(keys=[
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1524 ... {
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1525 ... 'username': 'johndoe',
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1526 ... },
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1527 ... {
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1528 ... 'username': 'jane',
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1529 ... },
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1530 ... {
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1531 ... 'username': 'fred',
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1532 ... },
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1533 ... ])
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1534 >>> for res in results:
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1535 ... print res['first_name']
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1536 'John'
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1537 'Jane'
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diff changeset
1538 'Fred'
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1539
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1540 """
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1541 # We pass the keys to the constructor instead, so it can maintain it's
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diff changeset
1542 # own internal state as to what keys have been processed.
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1543 results = BatchGetResultSet(keys=keys, max_batch_get=self.max_batch_get)
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1544 results.to_call(self._batch_get, consistent=consistent, attributes=attributes)
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1545 return results
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1546
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1547 def _batch_get(self, keys, consistent=False, attributes=None):
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1548 """
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1549 The internal method that performs the actual batch get. Used extensively
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1550 by ``BatchGetResultSet`` to perform each (paginated) request.
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1551 """
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1552 items = {
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1553 self.table_name: {
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1554 'Keys': [],
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1555 },
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1556 }
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1557
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1558 if consistent:
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1559 items[self.table_name]['ConsistentRead'] = True
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1560
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1561 if attributes is not None:
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1562 items[self.table_name]['AttributesToGet'] = attributes
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1563
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1564 for key_data in keys:
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1565 raw_key = {}
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1566
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1567 for key, value in key_data.items():
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1568 raw_key[key] = self._dynamizer.encode(value)
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1569
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1570 items[self.table_name]['Keys'].append(raw_key)
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1571
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1572 raw_results = self.connection.batch_get_item(request_items=items)
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1573 results = []
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1574 unprocessed_keys = []
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1575
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1576 for raw_item in raw_results['Responses'].get(self.table_name, []):
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1577 item = Item(self)
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1578 item.load({
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1579 'Item': raw_item,
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1580 })
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1581 results.append(item)
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1582
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1583 raw_unproccessed = raw_results.get('UnprocessedKeys', {})
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1584
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1585 for raw_key in raw_unproccessed.get('Keys', []):
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1586 py_key = {}
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1587
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1588 for key, value in raw_key.items():
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1589 py_key[key] = self._dynamizer.decode(value)
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1590
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1591 unprocessed_keys.append(py_key)
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1592
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1593 return {
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1594 'results': results,
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1595 # NEVER return a ``last_key``. Just in-case any part of
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1596 # ``ResultSet`` peeks through, since much of the
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1597 # original underlying implementation is based on this key.
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1598 'last_key': None,
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1599 'unprocessed_keys': unprocessed_keys,
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1600 }
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1601
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1602 def count(self):
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1603 """
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1604 Returns a (very) eventually consistent count of the number of items
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1605 in a table.
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1606
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1607 Lag time is about 6 hours, so don't expect a high degree of accuracy.
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1608
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1609 Example::
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1610
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1611 >>> users.count()
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1612 6
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1613
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1614 """
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1615 info = self.describe()
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1616 return info['Table'].get('ItemCount', 0)
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1617
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1618
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1619 class BatchTable(object):
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1620 """
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1621 Used by ``Table`` as the context manager for batch writes.
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1622
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1623 You likely don't want to try to use this object directly.
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1624 """
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1625 def __init__(self, table):
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1626 self.table = table
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1627 self._to_put = []
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1628 self._to_delete = []
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1629 self._unprocessed = []
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1630
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1631 def __enter__(self):
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1632 return self
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1633
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1634 def __exit__(self, type, value, traceback):
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1635 if self._to_put or self._to_delete:
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1636 # Flush anything that's left.
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1637 self.flush()
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1638
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1639 if self._unprocessed:
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1640 # Finally, handle anything that wasn't processed.
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1641 self.resend_unprocessed()
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1642
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1643 def put_item(self, data, overwrite=False):
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1644 self._to_put.append(data)
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1645
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1646 if self.should_flush():
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1647 self.flush()
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1648
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1649 def delete_item(self, **kwargs):
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1650 self._to_delete.append(kwargs)
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1651
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1652 if self.should_flush():
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1653 self.flush()
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1654
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1655 def should_flush(self):
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1656 if len(self._to_put) + len(self._to_delete) == 25:
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1657 return True
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1658
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1659 return False
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1660
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1661 def flush(self):
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1662 batch_data = {
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1663 self.table.table_name: [
d67268158946 planemo upload commit a3f181f5f126803c654b3a66dd4e83a48f7e203b
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1664 # We'll insert data here shortly.
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1665 ],
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1666 }
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1667
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1668 for put in self._to_put:
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1669 item = Item(self.table, data=put)
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1670 batch_data[self.table.table_name].append({
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1671 'PutRequest': {
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1672 'Item': item.prepare_full(),
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1673 }
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1674 })
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bcclaywell
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1675
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1676 for delete in self._to_delete:
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1677 batch_data[self.table.table_name].append({
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1678 'DeleteRequest': {
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1679 'Key': self.table._encode_keys(delete),
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1680 }
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1681 })
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1682
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1683 resp = self.table.connection.batch_write_item(batch_data)
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1684 self.handle_unprocessed(resp)
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1685
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1686 self._to_put = []
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1687 self._to_delete = []
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1688 return True
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1689
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1690 def handle_unprocessed(self, resp):
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1691 if len(resp.get('UnprocessedItems', [])):
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1692 table_name = self.table.table_name
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1693 unprocessed = resp['UnprocessedItems'].get(table_name, [])
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1694
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1695 # Some items have not been processed. Stow them for now &
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1696 # re-attempt processing on ``__exit__``.
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bcclaywell
parents:
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1697 msg = "%s items were unprocessed. Storing for later."
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bcclaywell
parents:
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1698 boto.log.info(msg % len(unprocessed))
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bcclaywell
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1699 self._unprocessed.extend(unprocessed)
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1700
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1701 def resend_unprocessed(self):
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1702 # If there are unprocessed records (for instance, the user was over
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1703 # their throughput limitations), iterate over them & send until they're
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1704 # all there.
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1705 boto.log.info(
d67268158946 planemo upload commit a3f181f5f126803c654b3a66dd4e83a48f7e203b
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parents:
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1706 "Re-sending %s unprocessed items." % len(self._unprocessed)
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bcclaywell
parents:
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1707 )
d67268158946 planemo upload commit a3f181f5f126803c654b3a66dd4e83a48f7e203b
bcclaywell
parents:
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1708
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parents:
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1709 while len(self._unprocessed):
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bcclaywell
parents:
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1710 # Again, do 25 at a time.
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bcclaywell
parents:
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1711 to_resend = self._unprocessed[:25]
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bcclaywell
parents:
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1712 # Remove them from the list.
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bcclaywell
parents:
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1713 self._unprocessed = self._unprocessed[25:]
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bcclaywell
parents:
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1714 batch_data = {
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bcclaywell
parents:
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1715 self.table.table_name: to_resend
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bcclaywell
parents:
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1716 }
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bcclaywell
parents:
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1717 boto.log.info("Sending %s items" % len(to_resend))
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bcclaywell
parents:
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1718 resp = self.table.connection.batch_write_item(batch_data)
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bcclaywell
parents:
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1719 self.handle_unprocessed(resp)
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bcclaywell
parents:
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1720 boto.log.info(
d67268158946 planemo upload commit a3f181f5f126803c654b3a66dd4e83a48f7e203b
bcclaywell
parents:
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1721 "%s unprocessed items left" % len(self._unprocessed)
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parents:
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1722 )