SQL best practices

As described in Query execution plans, SQL compiler transforms a SQL statement into a query execution plan, which is used to obtain the results of the query. This page describes best practices for constructing SQL statements to help Spanner find efficient execution plans.

The example SQL statements shown in this page use the following sample schema:

GoogleSQL

CREATE TABLE Singers (
 SingerId   INT64 NOT NULL,
 FirstName  STRING(1024),
 LastName   STRING(1024),
 SingerInfo BYTES(MAX),
 BirthDate  DATE
) PRIMARY KEY (SingerId);

CREATE TABLE Albums (
 SingerId     INT64 NOT NULL,
 AlbumId      INT64 NOT NULL,
 AlbumTitle   STRING(MAX),
 ReleaseDate  DATE
) PRIMARY KEY (SingerId, AlbumId),
INTERLEAVE IN PARENT Singers ON DELETE CASCADE;

For the complete SQL reference, refer to Statement syntax, Functions and operators, and Lexical structure and syntax.

PostgreSQL

CREATE TABLE Singers (
 SingerId   BIGINT PRIMARY KEY,
 FirstName  VARCHAR(1024),
 LastName   VARCHAR(1024),
 SingerInfo BYTEA,
 BirthDate  TIMESTAMPTZ
);

CREATE TABLE Albums (
 SingerId        BIGINT NOT NULL,
 AlbumId         BIGINT NOT NULL,
 AlbumTitle      VARCHAR(1024),
 ReleaseDate     DATE,
 PRIMARY KEY(SingerId, AlbumId),
 FOREIGN KEY (SingerId) REFERENCES Singers(SingerId)
) INTERLEAVE IN PARENT Singers ON DELETE CASCADE;

For more information, refer to The PostgreSQL language in Spanner.

Use query parameters

Spanner supports query parameters to increase performance and help prevent SQL injection when queries are constructed using user input. You can use query parameters as substitutes for arbitrary expressions but not as substitutes for identifiers, column names, table names, or other parts of the query.

Parameters can appear anywhere that a literal value is expected. The same parameter name can be used more than once in a single SQL statement.

In summary, query parameters support query execution in the following ways:

• Pre-optimized plans: Queries that use parameters can be executed faster on each invocation because the parameterization makes it easier for Spanner to cache the execution plan.
• Simplified query composition: You do not need to escape string values when providing them in query parameters. Query parameters also reduce the risk of syntax errors.
• Security: Query parameters make your queries more secure by protecting you from various SQL injection attacks. This protection is especially important for queries that you construct from user input.

Understand how Spanner executes queries

Note: Spanner Studio (formerly labeled Query in the Google Cloud console) supports SQL, DML, and DDL operations in a single editor. For more information, see Manage your data using the Google Cloud console.

Spanner lets you query databases using declarative SQL statements that specify what data you want to retrieve. If you want to understand how Spanner obtains the results, examine the execution plan for the query. A query execution plan displays the computational cost associated with each step of the query. Using those costs, you can debug query performance issues and optimize your query. To learn more, see Query execution plans.

You can retrieve query execution plans through the Google Cloud console or the client libraries.

To get a query execution plan for a specific query using the Google Cloud console, follow these steps:

1. Open the Spanner instances page.

   Go to Spanner instances

2. Select the names of the Spanner instance and the database that you want to query.

3. Click Spanner Studio in the left navigation panel.

4. Type the query in the text field, and then click Run query.

5. Click Explanation
   . The Google Cloud console displays a visual execution plan for your query.

   

For more information on how to understand visual plans and use them to debug your queries, see Tune a query using the query plan visualizer.

You can also view samples of historic query plans and compare the performance of a query over time for certain queries. To learn more, see Sampled query plans.

Use secondary indexes

Like other relational databases, Spanner offers secondary indexes, which you can use to retrieve data using either a SQL statement or Spanner's read interface. The more common way to fetch data from an index is to use the Spanner Studio. Using a secondary index in a SQL query lets you specify how you want Spanner to obtain the results. Specifying a secondary index can speed up query execution.

For example, suppose you wanted to fetch the IDs of all the singers with a specific last name. One way to write such a SQL query is:

SELECT s.SingerId
FROM Singers AS s
WHERE s.LastName = 'Smith';

This query would return the results that you expect, but it might take a long time to return the results. The timing would depend on the number of rows in the Singers table and how many satisfy the predicate WHERE s.LastName = 'Smith'. If there is no secondary index that contains the LastName column to read from, the query plan would read the entire Singers table to find rows that match the predicate. Reading the entire table is called a full table scan. A full table scan is an expensive way to obtain the results when the table contains only a small percentage of Singers with that last name.

You can improve the performance of this query by defining a secondary index on the last name column:

CREATE INDEX SingersByLastName ON Singers (LastName);

Because the secondary index SingersByLastName contains the indexed table column LastName and the primary key column SingerId, Spanner can fetch all the data from the much smaller index table instead of scanning the full Singers table.

In this scenario, Spanner automatically uses the secondary index SingersByLastName when executing the query (as long as three days have passed since database creation; see A note about new databases). However, it's best to explicitly tell Spanner to use that index by specifying an index directive in the FROM clause:

GoogleSQL

SELECT s.SingerId
FROM Singers@{FORCE_INDEX=SingersByLastName} AS s
WHERE s.LastName = 'Smith';

PostgreSQL

 SELECT s.SingerId
FROM Singers /*@ FORCE_INDEX=SingersByLastName */ AS s
WHERE s.LastName = 'Smith';

Now suppose you also wanted to fetch the singer's first name in addition to the ID. Even though the FirstName column is not contained in the index, you should still specify the index directive as before:

GoogleSQL

SELECT s.SingerId, s.FirstName
FROM Singers@{FORCE_INDEX=SingersByLastName} AS s
WHERE s.LastName = 'Smith';

PostgreSQL

SELECT s.SingerId, s.FirstName
FROM Singers /*@ FORCE_INDEX=SingersByLastName */ AS s
WHERE s.LastName = 'Smith';

You still get a performance benefit from using the index because Spanner doesn't need to do a full table scan when executing the query plan. Instead, it selects the subset of rows that satisfy the predicate from the SingersByLastName index, and then does a lookup from the base table Singers to fetch the first name for only that subset of rows.

If you want Spanner to not have to fetch any rows from the base table at all, you can store a copy of the FirstName column in the index itself:

GoogleSQL

CREATE INDEX SingersByLastName ON Singers (LastName) STORING (FirstName);

PostgreSQL

CREATE INDEX SingersByLastName ON Singers (LastName) INCLUDE (FirstName);

Using a STORING clause (for the GoogleSQL dialect) or an INCLUDE clause (for the PostgreSQL dialect) like this costs extra storage but it provides the following advantages:

• SQL queries that use the index and select columns stored in the STORING or INCLUDE clause do not require an extra join to the base table.
• Read calls that use the index can read columns stored in the STORING or INCLUDE clause.

The preceding examples illustrate how secondary indexes can speed up queries when the rows chosen by the WHERE clause of a query can be quickly identified using the secondary index.

Another scenario in which secondary indexes can offer performance benefits is for certain queries that return ordered results. For example, suppose you want to fetch all album titles and their release dates in ascending order of release date and descending order of album title. You could write a SQL query as follows:

SELECT a.AlbumTitle, a.ReleaseDate
FROM Albums AS a
ORDER BY a.ReleaseDate, a.AlbumTitle DESC;

Without a secondary index, this query requires a potentially expensive sorting step in the execution plan. You could speed up query execution by defining this secondary index:

CREATE INDEX AlbumsByReleaseDateTitleDesc on Albums (ReleaseDate, AlbumTitle DESC);

Then, rewrite the query to use the secondary index:

GoogleSQL

SELECT a.AlbumTitle, a.ReleaseDate
FROM Albums@{FORCE_INDEX=AlbumsByReleaseDateTitleDesc} AS a
ORDER BY a.ReleaseDate, a.AlbumTitle DESC;

PostgreSQL

SELECT a.AlbumTitle, a.ReleaseDate
FROM Albums /*@ FORCE_INDEX=AlbumsByReleaseDateTitleDesc */ AS s
ORDER BY a.ReleaseDate, a.AlbumTitle DESC;

This query and index definition meet both of the following criteria:

• To remove the sorting step, ensure that the column list in the ORDER BY clause is a prefix of the index key list.
• To avoid joining back from the base table to fetch any missing columns, ensure that the index covers all columns in the table that the query uses.

Although secondary indexes can speed up common queries, adding secondary indexes can add latency to your commit operations, because each secondary index typically requires involving an extra node in each commit. For most workloads, having a few secondary indexes is fine. However, you should consider whether you care more about read or write latency, and consider which operations are most critical for your workload. Benchmark your workload to ensure that it's performing as you expect.

For the complete reference on secondary indexes, refer to Secondary indexes.

Optimize scans

Certain Spanner queries might benefit from using a batch-oriented processing method when scanning data rather than the more common row-oriented processing method. Processing scans in batches is a more efficient way to process large volumes of data all at once, and it allows queries to achieve lower CPU utilization and latency.

The Spanner scan operation always starts execution in row-oriented mode. During this time, Spanner collects several runtime metrics. Then, Spanner applies a set of heuristics based on the result of these metrics to determine the optimal scan mode. When appropriate, Spanner switches to a batch-oriented processing mode to help improve scan throughput and performance.

Common uses cases

Queries with the following characteristics generally benefit from the use of batch-oriented processing:

• Large scans over infrequently updated data.
• Scans with predicates on fixed width columns.
• Scans with large seek counts. (A seek uses an index to retrieve records.)

Use cases without performance gains

Not all queries benefit from batch-oriented processing. The following query types perform better with row-oriented scan processing:

• Point lookup queries: queries that only fetch one row.
• Small scan queries: table scans that only scan a few rows unless they have large seek counts.
• Queries that use LIMIT.
• Queries that read high churn data: queries in which more than ~10% of the data read is frequently updated.
• Queries with rows containing large values: large value rows are those containing values larger than 32,000 bytes (pre-compression) in a single column.

How to check the scan method used by a query

To check if your query uses batch-oriented processing, row-oriented processing, or is automatically switching between the two scan methods:

1. Go to the Spanner Instances page in the Google Cloud console.

   Go to the Instances page

2. Click the name of the instance with the query that you want to investigate.

3. Under the Databases table, click the database with the query that you want to investigate.

4. In the Navigation menu, click Spanner Studio.

5. Open a new tab by clicking New SQL editor tab or New tab.

6. When the query editor appears, write your query.

7. Click Run.

   Spanner runs the query and shows the results.

8. Click the Explanation tab below the query editor.

   Spanner shows a query plan execution plan visualizer. Each card on the graph represents an iterator.

9. Click the Table scan iterator card to open an information panel.

   The information panel shows contextual information about the selected scan. The scan method is shown on this card. Automatic indicates Spanner determines the scanning method. Other possible values include Vectorized for batch-oriented processing and Scalar for row-oriented processing.

   

How to enforce the scan method used by a query

To optimize query performance, Spanner chooses the optimal scan method for your query. We recommend that you use this default scan method. However, there might be scenarios where you might want to enforce a specific type of scan method.

How to enforce batch-oriented scanning

You can enforce batch-oriented scanning at the table level and statement level.

To enforce the batch-oriented scan method at the table level, use a table hint in your query:

GoogleSQL

  SELECT ...
  FROM (t1@{SCAN_METHOD=BATCH} JOIN t2 ON ...)
  WHERE ...

PostgreSQL

  SELECT ...
  FROM (t1/*@ scan_method=batch */ JOIN t2 on ...)
  WHERE ...

To enforce the batch-oriented scan method at the statement level, use a statement hint in your query:

GoogleSQL

  @{SCAN_METHOD=BATCH}
  SELECT ...
  FROM ...
  WHERE ...

PostgreSQL

  /*@ scan_method=batch */
  SELECT ...
  FROM ...
  WHERE ...

How to disable automatic scanning and enforce row-oriented scanning

Although we don't recommend disabling the automatic scanning method set by Spanner, you might decide to disable it and use the row-oriented scanning method for troubleshooting purposes, such as diagnosing latency.

To disable the automatic scan method and enforce row processing at the table level, use a table hint in your query:

GoogleSQL

  SELECT ...
  FROM (t1@{SCAN_METHOD=ROW} JOIN t2 ON ...)
  WHERE ...

PostgreSQL

  SELECT ...
  FROM (t1/*@ scan_method=row */ JOIN t2 on ...)
  WHERE ...

To disable the automatic scan method and enforce row processing at the statement level, use a statement hint in your query:

GoogleSQL

  @{SCAN_METHOD=ROW}
  SELECT ...
  FROM ...
  WHERE ...

PostgreSQL

  /*@ scan_method=row */
  SELECT ...
  FROM ...
  WHERE ...

Optimize range key lookups

A common use of a SQL query is to read multiple rows from Spanner based on a list of known keys.

The following best practices help you write efficient queries when fetching data by a range of keys:

• If the list of keys is sparse and not adjacent, use query parameters and UNNEST to construct your query.

  For example, if your key list is {1, 5, 1000}, write the query like this:

  GoogleSQL

  SELECT *
  FROM Table AS t
  WHERE t.Key IN UNNEST (@KeyList)

  PostgreSQL

  SELECT *
  FROM Table AS t
  WHERE t.Key IN UNNEST ($1)

  Notes:

  • The array UNNEST operator flattens an input array into rows of elements.

  • The query parameter, which is @KeyList for GoogleSQL and $1 for PostgreSQL, can speed up your query as discussed in the preceding best practice.

• If the list of keys is adjacent and within a range, specify the lower and higher limits of the key range in the WHERE clause.

  For example, if your key list is {1,2,3,4,5}, construct the query as follows:

  GoogleSQL

  SELECT *
  FROM Table AS t
  WHERE t.Key BETWEEN @min AND @max

  Note: Here, @min and @max are query parameters that are bound to the values 1 and 5, respectively.

  PostgreSQL

  SELECT *
  FROM Table AS t
  WHERE t.Key BETWEEN $1 AND $2

  Note: Here, $1 and $2 are query parameters that are bound to the values 1 and 5, respectively.

  This query is only more efficient if the keys in the key range are adjacent. In other words, if your key list is {1, 5, 1000}, don't specify the lower and higher limits like in the preceding query because the resulting query would scan through every value between 1 and 1000.

Optimize joins

Join operations can be expensive because they can significantly increase the number of rows that your query needs to scan, which results in slower queries. In addition to the techniques that you're accustomed to using in other relational databases to optimize join queries, here are some best practices for a more efficient JOIN when using Spanner SQL:

• If possible, join data in interleaved tables by primary key. For example:

  SELECT s.FirstName, a.ReleaseDate
  FROM Singers AS s JOIN Albums AS a ON s.SingerId = a.SingerId;

  The rows in the interleaved table Albums are guaranteed to be physically stored in the same splits as the parent row in Singers, as discussed in Schema and Data Model. Therefore, joins can be completed locally without sending lots of data across the network.

• Use the join directive if you want to force the order of the join. For example:

  GoogleSQL

  SELECT *
  FROM Singers AS s JOIN@{FORCE_JOIN_ORDER=TRUE} Albums AS a
  ON s.SingerId = a.Singerid
  WHERE s.LastName LIKE '%x%' AND a.AlbumTitle LIKE '%love%';

  PostgreSQL

  SELECT *
  FROM Singers AS s JOIN/*@ FORCE_JOIN_ORDER=TRUE */ Albums AS a
  ON s.SingerId = a.Singerid
  WHERE s.LastName LIKE '%x%' AND a.AlbumTitle LIKE '%love%';

  The join directive FORCE_JOIN_ORDER tells Spanner to use the join order specified in the query (that is, Singers JOIN Albums, not Albums JOIN Singers). The returned results are the same, regardless of the order that Spanner chooses. However, you might want to use this join directive if you notice in the query plan that Spanner has changed the join order and caused undesirable consequences, such as larger intermediate results, or has missed opportunities for seeking rows.

• Use a join directive to choose a join implementation. When you use SQL to query multiple tables, Spanner automatically uses a join method that is likely to make the query more efficient. However, Google advises you to test with different join algorithms. Choosing the right join algorithm can improve latency, memory consumption, or both. This query demonstrates the syntax for using a JOIN directive with the JOIN_METHOD hint to choose a HASH JOIN:

  GoogleSQL

  SELECT *
  FROM Singers s JOIN@{JOIN_METHOD=HASH_JOIN} Albums AS a
  ON a.SingerId = a.SingerId

  PostgreSQL

  SELECT *
  FROM Singers s JOIN/*@ JOIN_METHOD=HASH_JOIN */ Albums AS a
  ON a.SingerId = a.SingerId

• If you're using a HASH JOIN or APPLY JOIN and if you have a WHERE clause that is highly selective on one side of your JOIN, put the table that produces the smallest number of rows as the first table in the FROM clause of the join. This structure helps because currently in HASH JOIN, Spanner always picks the left-hand side table as build and the right-hand side table as probe. Similarly, for APPLY JOIN, Spanner picks the left-hand side table as outer and the right-hand side table as inner. See more about these join types: Hash join and Apply join.

• For queries that are critical for your workload, specify the most performant join method and join order in your SQL statements for more consistent performance.

Avoid large reads inside read-write transactions

Read-write transactions allow a sequence of zero or more reads or SQL queries, and can include a set of mutations, before a call to commit. To maintain the consistency of your data, Spanner acquires locks when reading and writing rows in your tables and indexes. For more information about locking, see Life of Reads and Writes.

Because of the way locking works in Spanner, performing a read or SQL query that reads a large number of rows (for example SELECT * FROM Singers) means that no other transactions can write to the rows that you've read until your transaction is either committed or aborted.

Furthermore, because your transaction is processing a large number of rows, it's likely to take longer than a transaction that reads a much smaller range of rows (for example SELECT LastName FROM Singers WHERE SingerId = 7), which further exacerbates the problem and reduces system throughput.

So, try to avoid large reads (for example, full table scans or massive join operations) in your transactions, unless you're willing to accept lower write throughput.

In some cases, the following pattern can yield better results:

1. Do your large reads inside a read-only transaction. Read-only transactions allow for higher aggregate throughput because they do not use locks.
2. Optional: Do any processing required on the data you just read.
3. Start a read-write transaction.
4. Verify that the critical rows have not changed values since you performed the read-only transaction in step 1.
   • If the rows have changed, roll back your transaction and start again at step 1.
   • If everything looks okay, commit your mutations.

One way to ensure that you're avoiding large reads in read-write transactions is to look at the execution plans that your queries generate.

Use ORDER BY to ensure the ordering of your SQL results

If you're expecting a certain ordering for the results of a SELECT query, explicitly include the ORDER BY clause. For example, if you want to list all singers in primary key order, use this query:

SELECT * FROM Singers
ORDER BY SingerId;

Spanner guarantees result ordering only if the ORDER BY clause is present in the query. In other words, consider this query without the ORDER BY:

SELECT * FROM Singers;

Spanner does not guarantee that the results of this query will be in primary key order. Furthermore, the ordering of results can change at any time and is not guaranteed to be consistent from invocation to invocation. If a query has an ORDER BY clause, and Spanner uses an index that provides the required order, then Spanner doesn't explicitly sort the data. Therefore, don't worry about the performance impact of including this clause. You can check whether an explicit sort operation is included in the execution by looking at the query plan.

Use STARTS_WITH instead of LIKE

Because Spanner does not evaluate parameterized LIKE patterns until execution time, Spanner must read all rows and evaluate them against the LIKE expression to filter out rows that do not match.

When a LIKE pattern has the form foo% (for example, it starts with a fixed string and ends with a single wildcard percent) and the column is indexed, use STARTS_WITH instead of LIKE. This option allows Spanner to more effectively optimize the query execution plan.

Not recommended:

GoogleSQL

SELECT a.AlbumTitle FROM Albums a
WHERE a.AlbumTitle LIKE @like_clause;

Note: This example assumes @like_clause is bound to 'Love%'.

PostgreSQL

SELECT a.AlbumTitle FROM Albums a
WHERE a.AlbumTitle LIKE $1;

Note: This example assumes $2 is bound to 'Love%'.

Recommended:

GoogleSQL

SELECT a.AlbumTitle FROM Albums a
WHERE STARTS_WITH(a.AlbumTitle, @prefix);

Note: This example assumes @prefix is bound to 'Love'. This query is more efficient than the previous query. It runs faster if an index is defined on Albums.AlbumTitle.

PostgreSQL

SELECT a.AlbumTitle FROM Albums a
WHERE STARTS_WITH(a.AlbumTitle, $2);

Note: This example assumes $1 is bound to 'Love'. This query is more efficient than the previous query. It runs faster if an index is defined on Albums.AlbumTitle.

Use commit timestamps

If your application needs to query data written after a particular time, add commit timestamp columns to the relevant tables. Commit timestamps enable a Spanner optimization that can reduce the I/O of queries whose WHERE clauses restrict results to rows written more recently than a specific time.

Learn more about this optimization with GoogleSQL-dialect databases or with PostgreSQL-dialect databases.