N1QL Queries from the SDK
You can query for documents in Couchbase using the N1QL query language, a language based on SQL, but designed for structured and flexible JSON documents. Querying can solve typical programming tasks such as finding a user profile by email address, facebook login, or user ID.
These pages cover the Beta release of the Couchbase Python SDK. The API interface is stable, but could change for bug fixes during the Beta process. New features are likely to be added.
Documentation is incomplete, subject to change, and could contain broken links.
Our query service uses N1QL, which will be fairly familiar to anyone who’s used any dialect of SQL. Further resources for learning about N1QL are listed at the bottom of the page. Before you get started you may wish to checkout the N1QL intro page, or just dive in with a query against our "travel-sample" data set. In this case, note that before you can query a bucket, you must define at least one index. You can define a primary index on a bucket. When a primary index is defined you can issue non-covered queries on the bucket as well.
Use cbq, our interactive Query shell. Open it, and enter the following:
CREATE PRIMARY INDEX ON `travel-sample`
or replace travel-sample with a different Bucket name to build an index on a different dataset.
The default installation places cbq in
Placeholders allow you to specify variable constraints for an otherwise constant query. There are two variants of placeholders: postional and named parameters. Positional parameters use an ordinal placeholder for substitution and named parameters use variables. A named or positional parameter is a placeholder for a value in the WHERE, LIMIT or OFFSET clause of a query. Note that both parameters and options are optional.
result = collection.query( "SELECT x.* FROM `default` WHERE x.Type=$1", 'User')
result = collection.query( "SELECT x.* FROM `default` WHERE x.Type=$type", type='User')
The complete code for this page’s example can be found at n1ql_ops.py.
In most cases your query will return more than one result, and you may be looking to iterate over those results:
result = cluster.query( "SELECT x.* FROM `default` WHERE x.Type=$1", 'User') # iterate over rows for row in result: # each row is an instance of the query call name = row['username'] age = row['age']
Setting a staleness parameter for queries, with
scan_consistency, enables a tradeoff between latency and (eventual) consistency.
A N1QL query using the default Not Bounded scan consistency will not wait for any indexes to finish updating before running the query and returning results, meaning that results are returned quickly, but the query will not return any documents that are yet to be indexed.
With Scan Consistency set to RequestPlus, all document changes and index updates are processed before the query is run. Select this when consistency is always more important than performance.
For a middle ground, AtPlus is a "read your own write" (RYOW) option, which means it just waits for the new documents that you specify to be indexed, rather than an entire index of multiple documents.
# create / update document (mutation) upsert_result = collection.upsert("id", dict( name = "Mike", type = "User" )) # create mutation state from mutation results state = MutationState() state.add_results(upsert_result) # use mutation state with query option from couchbase_core.n1ql import N1QLQuery query=N1QLQuery( "SELECT x.* FROM `default` WHERE x.Type=$1", 'User') query.consistent_with(state) result = cluster.query(query)
By default, the Python SDK will stream the result set from the server, where the client will start a persistent connection with the server and only read the header until the Rows are enumerated; then, each row or JSON object will be de-serialized one at a time.
This decreases pressure on Garbage Collection and helps to prevent OutOfMemory errors.
|N1QL is not the only query option in Couchbase. Be sure to check that concept-docs:http-services.adoc.|
For a deeper dive into N1QL from the SDK, refer to our N1QL SDK concept doc.
The Server doc N1QL intro introduces a complete guide to the N1QL language, including all of the latest additions.
The N1QL interactive tutorial is a good introduction to the basics of N1QL use.
For scaling up queries, be sure to read up on Indexes.
N1QL is for operational queries; for analytical workloads, read more on concept-docs:http-services.adoc#Long-Running-Queries-&-Big-Data, our implementation of SQL++.