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Data Consistency in Datastore Queries
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Data consistency levels
Datastore queries can deliver their results at either of two consistency
levels:
In an eventually consistent query, the indexes used to gather the results are also accessed with eventual consistency. Consequently, such queries may sometimes return entities that no longer match the original query criteria, while strongly consistent queries are always transactionally consistent.
Datastore query data consistency
Queries return their results with different levels of consistency guarantee, depending on the nature of the query:
- Ancestor queries (those within an
entity group)
are strongly consistent by default, but can instead be made eventually
consistent by setting the Datastore read policy (see below).
- Non-ancestor queries are always eventually consistent.
Fetching an entity by key, which is also called "lookup by key", is strongly
consistent.
Setting the Datastore read policy
To improve performance, you can set the Datastore read policy so that all reads and queries are eventually consistent. (The API also allows you to explicitly set a strong consistency policy, but this setting will have no practical effect, since non-ancestor queries are always eventually consistent regardless of policy.)
You can also set the Datastore
call deadline, which is the maximum time, in seconds, that the application will wait for Datastore to return a result before aborting with an error. The default deadline is 60 seconds; it is not currently possible to set it higher, but you can adjust it downward to ensure that a particular operation fails quickly (for instance, to return a faster response to the user).
To set the Datastore read policy and call deadline in Python, you pass them
as arguments to the
run()
,
get()
,
fetch()
, and
count()
methods of class
Query
or
GqlQuery
. For example:
for result in Employee.all().run(limit=5,
read_policy=db.EVENTUAL_CONSISTENCY,
deadline=5):
# Body of iterative loop
What's next?
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Last updated 2025-08-25 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eDatastore queries operate at two consistency levels: strongly consistent, which guarantees the freshest data but may take longer, and eventually consistent, which is generally faster but might return stale data.\u003c/p\u003e\n"],["\u003cp\u003eAncestor queries, which occur within an entity group, are strongly consistent by default but can be made eventually consistent by adjusting the Datastore read policy, whereas non-ancestor queries are always eventually consistent.\u003c/p\u003e\n"],["\u003cp\u003eFetching an entity by key, also known as "lookup by key", provides strong consistency in retrieving data.\u003c/p\u003e\n"],["\u003cp\u003eThe Datastore read policy can be set to ensure all reads and queries are eventually consistent, optimizing performance, and while a strong consistency policy can be set, it has no effect on non-ancestor queries, as they remain eventually consistent.\u003c/p\u003e\n"],["\u003cp\u003eThe Datastore call deadline, the maximum time an application waits for a result, defaults to 60 seconds but can be reduced to ensure faster operation failures, thus allowing the possibility of faster user responses.\u003c/p\u003e\n"]]],[],null,["# Data Consistency in Datastore Queries\n\nData consistency levels\n-----------------------\n\nDatastore queries can deliver their results at either of two consistency\nlevels:\n\n- [*Strongly consistent*](https://en.wikipedia.org/wiki/Strong_consistency) queries guarantee the freshest results, but may take longer to complete.\n- [*Eventually consistent*](https://en.wikipedia.org/wiki/Eventual_consistency) queries generally run faster, but may occasionally return stale results.\n\nIn an eventually consistent query, the indexes used to gather the results are also accessed with eventual consistency. Consequently, such queries may sometimes return entities that no longer match the original query criteria, while strongly consistent queries are always transactionally consistent.\n\nDatastore query data consistency\n--------------------------------\n\nQueries return their results with different levels of consistency guarantee, depending on the nature of the query:\n\n- [Ancestor queries](/appengine/docs/legacy/standard/python/datastore/queries#ancestor_queries) (those within an [entity group](/appengine/docs/legacy/standard/python/datastore/entities#Ancestor_paths)) are strongly consistent by default, but can instead be made eventually consistent by setting the Datastore read policy (see below).\n- Non-ancestor queries are always eventually consistent.\n\nFetching an entity by key, which is also called \"lookup by key\", is strongly\nconsistent.\n\n\u003cbr /\u003e\n\nSetting the Datastore read policy\n---------------------------------\n\nTo improve performance, you can set the Datastore *read policy* so that all reads and queries are eventually consistent. (The API also allows you to explicitly set a strong consistency policy, but this setting will have no practical effect, since non-ancestor queries are always eventually consistent regardless of policy.)\nYou can also set the Datastore *call deadline* , which is the maximum time, in seconds, that the application will wait for Datastore to return a result before aborting with an error. The default deadline is 60 seconds; it is not currently possible to set it higher, but you can adjust it downward to ensure that a particular operation fails quickly (for instance, to return a faster response to the user).\n\n\u003cbr /\u003e\n\nTo set the Datastore read policy and call deadline in Python, you pass them as arguments to the [`run()`](/appengine/docs/legacy/standard/python/datastore/queryclass#Query_run), [`get()`](/appengine/docs/legacy/standard/python/datastore/queryclass#Query_get), [`fetch()`](/appengine/docs/legacy/standard/python/datastore/queryclass#Query_fetch), and [`count()`](/appengine/docs/legacy/standard/python/datastore/queryclass#Query_count) methods of class [`Query`](/appengine/docs/legacy/standard/python/datastore/queryclass) or [`GqlQuery`](/appengine/docs/legacy/standard/python/datastore/gqlqueryclass). For example:\n\n\u003cbr /\u003e\n\n for result in Employee.all().run(limit=5,\n read_policy=db.EVENTUAL_CONSISTENCY,\n deadline=5):\n # Body of iterative loop\n\nWhat's next?\n------------\n\n- [Learn how to specify what a query returns and further control query results](/appengine/docs/legacy/standard/python/datastore/retrieving-query-results).\n- Learn the [common restrictions](/appengine/docs/legacy/standard/python/datastore/query-restrictions) for queries on Datastore.\n- Learn about [query cursors](/appengine/docs/legacy/standard/python/datastore/query-cursors), which allow an application to retrieve a query's results in convenient batches.\n- Learn the [basic syntax and structure of queries](/appengine/docs/legacy/standard/python/datastore/queries) for Datastore."]]