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Tuning python-oracledb

Some general tuning tips are:

  • Tune your application architecture.

    A general application goal is to reduce the number of round-trips <roundtrips> between python-oracledb and the database.

    For multi-user applications, make use of connection pooling. Create the pool once during application initialization. Do not oversize the pool, see connpooling . Use a session callback function to set session state, see Session CallBacks for Setting Pooled Connection State <sessioncallback>.

    Make use of efficient python-oracledb functions. For example, to insert multiple rows use Cursor.executemany() instead of Cursor.execute().

  • Tune your SQL statements. See the SQL Tuning Guide.

    Use bind variables <bind> to avoid statement reparsing.

    Tune Cursor.arraysize and Cursor.prefetchrows for each query, see Tuning Fetch Performance <tuningfetch>.

    Do simple optimizations like limiting the number of rows <rowlimit> and avoiding selecting columns not used in the application.

    It may be faster to work with simple scalar relational values than to use Oracle Database object types.

    Make good use of PL/SQL to avoid executing many individual statements from python-oracledb.

    Tune the Statement Cache <stmtcache>.

    Enable Client Result Caching <clientresultcache> for small lookup tables.

  • Tune your database. See the Database Performance Tuning Guide.
  • Tune your network. For example, when inserting or retrieving a large number of rows (or for large data), or when using a slow network, then tune the Oracle Network Session Data Unit (SDU) and socket buffer sizes, see Oracle Net Services: Best Practices for Database Performance and High Availability.
  • Do not commit or rollback unnecessarily. Use Connection.autocommit on the last of a sequence of DML statements.

Tuning Fetch Performance

To tune queries, you can adjust python-oracledb's internal buffer sizes to improve the speed of fetching rows across the network from the database, and to optimize memory usage. Regardless of which python-oracledb method <fetching> is used to get query results, internally all rows are fetched in batches from the database and buffered before being returned to the application. The internal buffer sizes can have a significant performance impact. The buffer sizes do not affect how or when rows are returned to your application. They do not affect the minimum or maximum number of rows returned by a query.

For best performance, tune "array fetching" with Cursor.arraysize and "row prefetching" with Cursor.prefetchrows before calling Cursor.execute(). Queries that return LOBs and similar types will never prefetch rows, so the prefetchrows value is ignored in those cases.

The common query tuning scenario is for SELECT statements that return a large number of rows over a slow network. Increasing arraysize can improve performance by reducing the number of round-trips <roundtrips> to the database. However increasing this value increases the amount of memory required. Adjusting prefetchrows will also affect performance and memory usage.

Row prefetching and array fetching are both internal buffering techniques to reduce round-trips <roundtrips> to the database. The difference is when the buffering occurs. Internally python-oracledb performs separate "execute SQL statement" and "fetch data" steps. Prefetching allows query results to be returned to the application when the acknowledgment of successful statement execution is returned from the database. This means that the subsequent internal "fetch data" operation does not always need to make a round-trip to the database because rows are already buffered in python-oracledb or in the Oracle Client libraries. Reducing round-trips helps performance and scalability. An overhead of prefetching when using the python-oracledb Thick mode is the need for additional data copies from Oracle Client's prefetch buffer.

Choosing values for arraysize and prefetchrows

The best Cursor.arraysize and Cursor.prefetchrows values can be found by experimenting with your application under the expected load of normal application use. This is because the cost of the extra memory copy from the prefetch buffer when fetching a large quantity of rows or very "wide" rows may outweigh the cost of a round-trip for a single python-oracledb user on a fast network. However under production application load, the reduction of round-trips may help performance and overall system scalability. The documentation in round-trips <roundtrips> shows how to measure round-trips.

Here are some suggestions for tuning:

  • To tune queries that return an unknown number of rows, estimate the number of rows returned and start with an appropriate Cursor.arraysize value. The default is 100. Then set Cursor.prefetchrows to the arraysize value. For example:

    cur = connection.cursor()

cur.prefetchrows = 1000
cur.arraysize = 1000

for row in cur.execute("SELECT * FROM very_big_table"):
    print(row)

    Adjust the values as needed for performance, memory and round-trip usage. Do not make the sizes unnecessarily large. For a large quantity of rows or very "wide" rows on fast networks you may prefer to leave prefetchrows at its default value of 2. Keep arraysize as big, or bigger than, prefetchrows.

  • If you are fetching a fixed number of rows, set arraysize to the number of expected rows, and set prefetchrows to one greater than this value. Adding one removes the need for a round-trip to check for end-of-fetch. For example, if you are querying 20 rows, perhaps to display a page <rowlimit> of data, then set prefetchrows to 21 and arraysize to 20:

    cur = connection.cursor()

cur.prefetchrows = 21
cur.arraysize = 20

for row in cur.execute("""
    SELECT last_name
       FROM employees
       ORDER BY last_name
       OFFSET 0 ROWS FETCH NEXT 20 ROWS ONLY"""):
    print(row)

    This will return all rows for the query in one round-trip.

  • If you know that a query returns just one row then set Cursor.arraysize to 1 to minimize memory usage. The default prefetch value of 2 allows minimal round-trips for single-row queries:

    cur = connection.cursor()
cur.arraysize = 1
cur.execute("select * from MyTable where id = 1"):
row = cur.fetchone()
print(row)

Application Default Prefetchrows and Arraysize Values

Application-wide defaults can be set using defaults.prefetchrows and defaults.arraysize, for example:

import oracledb

oracledb.defaults.prefetchrows = 1000
oracledb.defaults.arraysize    = 1000

When using python-oracledb in the Thick mode, prefetching can also be tuned in an external oraaccess.xml <optclientfiles> file, which may be useful for tuning an application when modifying its code is not feasible.

Setting the sizes with oracledb.defaults attributes or with oraaccess.xml will affect the whole application, so it should not be the first tuning choice.

Changing Prefetchrows and Arraysize for Re-executed Statements

In python-oracledb, the arraysize and prefetchrows values are only examined when a statement is executed the first time. To change the values for a re-executed statement, create a new cursor. For example, to change arraysize:

array_sizes = (10, 100, 1000)
for size in array_sizes:
    cursor = connection.cursor()
    cursor.arraysize = size
    start = time.time()
    cursor.execute(sql).fetchall()
    elapsed = time.time() - start
    print("Time for", size, elapsed, "seconds")

Avoiding Premature Prefetching

There are two cases that will benefit from setting prefetchrows to 0:

  • When passing REF CURSORS into PL/SQL packages. Setting prefetchrows to 0 can stop rows being prematurely (and silently) fetched into the python-oracledb or Oracle Client (in python-oracledb Thick mode) internal buffer, making those rows unavailable to the PL/SQL code that receives the REF CURSOR.
  • When querying a PL/SQL function that uses PIPE ROW to emit rows at intermittent intervals. By default, several rows needs to be emitted by the function before python-oracledb can return them to the application. Setting prefetchrows to 0 helps give a consistent flow of data to the application.

Tuning Data Copies between Databases

One place where increasing arraysize is particularly useful is in copying data from one database to another:

# setup cursors
source_cursor = source_connection.cursor()
source_cursor.arraysize = 1000
target_cursor = target_connection.cursor()

# perform fetch and bulk insertion
source_cursor.execute("select * from MyTable")
while True:
    rows = source_cursor.fetchmany()
    if not rows:
        break
    target_cursor.executemany("insert into MyTable values (:1, :2)", rows)
    target_connection.commit()

Note that it may be preferable to use database links between the databases and use an INSERT INTO SELECT statement so that data is not copied to, and from, Python.

Tuning Fetching from REF CURSORS

In python-oracledb, REF CURSORS can also be tuned by setting the values of arraysize and prefetchrows. The prefetchrows value must be set before calling the PL/SQL procedure as the REF CURSOR is executed on the server.

For example:

# Set the arraysize and prefetch rows of the REF cursor
ref_cursor = connection.cursor()
ref_cursor.prefetchrows = 1000
ref_cursor.arraysize = 1000

# Perform the tuned fetch
sum_rows = 0
cursor.callproc("myrefcursorproc", [ref_cursor])
print("Sum of IntCol for", num_rows, "rows:")
for row in ref_cursor:
    sum_rows += row[0]
print(sum_rows)

Database Round-trips

A round-trip is defined as the trip from the Oracle Client libraries (used by python-oracledb) to the database and back. Calling each python-oracledb function, or accessing each attribute, will require zero or more round-trips. Along with tuning an application's architecture and tuning its SQL statements, a general performance and scalability goal is to minimize round-trips.

Some general tips for reducing round-trips are:

  • Tune Cursor.arraysize and Cursor.prefetchrows for each query.
  • Use Cursor.executemany() for optimal DML execution.
  • Only commit when necessary. Use Connection.autocommit on the last statement of a transaction.
  • For connection pools, use a callback to set connection state, see Session CallBacks for Setting Pooled Connection State <sessioncallback>.
  • Make use of PL/SQL procedures which execute multiple SQL statements instead of executing them individually from python-oracledb.
  • Use scalar types instead of Oracle Database object types.
  • Avoid overuse of Connection.ping().
  • Avoid setting ConnectionPool.ping_interval to 0 or a small value.
  • When using SODA <sodausermanual>, use pooled connections and enable the SODA metadata cache <sodametadatacache>.

Finding the Number of Round-Trips

Oracle's Automatic Workload Repository (AWR) reports show 'SQL*Net roundtrips to/from client' and are useful for finding the overall behavior of a system.

Sometimes you may wish to find the number of round-trips used for a specific application. Snapshots of the V$SESSTAT view taken before and after doing some work can be used for this:

SELECT ss.value, sn.display_name
FROM v$sesstat ss, v$statname sn
WHERE ss.sid = SYS_CONTEXT('USERENV','SID')
AND ss.statistic# = sn.statistic#
AND sn.name LIKE '%roundtrip%client%';

Statement Caching

Python-oracledb's Cursor.execute() and Cursor.executemany() functions use the Oracle Call Interface statement cache for efficient re-execution of statements. Statement caching lets Oracle Database cursors be used without re-parsing the statement. Statement caching also reduces metadata transfer costs between python-oracledb and the database. Performance and scalability are improved.

Each standalone or pooled connection has its own cache of statements with a default size of 20. The default size of the statement cache can be changed using the defaults.stmtcachesize attribute. The size can be set when creating connection pools or standalone connections. In general, set the statement cache size to the size of the working set of statements being executed by the application. To manually tune the cache, monitor the general application load and the Automatic Workload Repository (AWR) "bytes sent via SQL*Net to client" values. The latter statistic should benefit from not shipping statement metadata to python-oracledb. Adjust the statement cache size to your satisfaction. With Oracle Database 12c, or later, the statement cache size can be automatically tuned using an oraaccess.xml <optclientfiles> file.

Setting the Statement Cache

The statement cache size can be set globally with defaults.stmtcachesize:

import oracledb

oracledb.defaults.stmtcachesize = 40

The value can be overridden in an oracledb.connect() call, or when creating a pool with oracledb.create_pool(). For example:

oracledb.create_pool(user="scott", password=userpwd, dsn="dbhost.example.com/orclpb",
                     min=2, max=5, increment=1, stmtcachesize=50)

When using Oracle Client 21 (or later), changing the cache size with ConnectionPool.reconfigure() does not immediately affect connections previously acquired and currently in use. When those connections are subsequently released to the pool and re-acquired, they will then use the new value. When using Oracle Client prior to version 21, changing the pool's statement cache size has no effect on connections that already exist in the pool but will affect new connections that are subsequently created, for example when the pool grows.

Tuning the Statement Cache

In general, set the statement cache to the size of the working set of statements being executed by the application. SODA <sodausermanual> internally makes SQL calls, so tuning the cache is also beneficial for SODA applications.

With Oracle Client Libraries 12c, or later, the statement cache size can be automatically tuned with the Oracle Client Configuration oraaccess.xml file.

For manual tuning use views like V$SYSSTAT:

SELECT value FROM V$SYSSTAT WHERE name = 'parse count (total)'

Find the value before and after running application load to give the number of statement parses during the load test. Alter the statement cache size and repeat the test until you find a minimal number of parses.

If you have Automatic Workload Repository (AWR) reports you can monitor general application load and the "bytes sent via SQL*Net to client" values. The latter statistic should benefit from not shipping statement metadata to python-oracledb. Adjust the statement cache size and re-run the test to find the best cache size.

Disabling the Statement Cache

Statement caching can be disabled by setting the cache size to 0:

oracledb.stmtCacheSize = 0

Disabling the cache may be beneficial when the quantity or order of statements causes cache entries to be flushed before they get a chance to be reused. For example if there are more distinct statements than cache slots, and the order of statement execution causes older statements to be flushed from the cache before the statements are re-executed.

Disabling the statement cache may also be helpful in test and development environments. The statement cache can become invalid if connections remain open and database schema objects are recreated. This can also happen when a connection uses identical query text with different fetchAsString or fetchInfo data types. Applications can receive errors such as ORA-3106. After a statement execution error is returned once to the application, python-oracledb automatically drops that statement from the cache. This lets subsequent re-executions of the statement on that connection to succeed.

When it is inconvenient to pass statement text through an application, the Cursor.prepare() call can be used to avoid statement re-parsing. If the cache_statement parameter in the Cursor.prepare() method is True and the statement cache size is greater than 0, then the statements will be added to the cache, if not already present. If the cache_statement parameter in the Cursor.prepare() method is False and the statement cache size is greater than 0, then the statement will be removed from the statement cache (if present) or will not be cached (if not present). The subsequent execute() calls use the value None instead of the SQL text.

This feature can prevent a rarely executed statement from flushing a potential more frequently executed one from a full cache. For example, if a statement will only ever be executed once:

cursor.prepare("select user from dual", cache_statement = False)
cursor.execute(None)

Alternatively,

sql = "select user from dual"
cursor.prepare(sql, cache_statement=Fasle)
cursor.execute(sql)

Statements passed to ~Cursor.prepare() are also stored in the statement cache.

Client Result Caching (CRC)

Python-oracledb applications can use Oracle Database's Client Result Cache. The CRC enables client-side caching of SQL query (SELECT statement) results in client memory for immediate use when the same query is re-executed. This is useful for reducing the cost of queries for small, mostly static, lookup tables, such as for postal codes. CRC reduces network round-trips <roundtrips>, and also reduces database server CPU usage.

Note

Client Result Caching is only supported in the python-oracledb Thick mode. See enablingthick.

The cache is at the application process level. Access and invalidation is managed by the Oracle Client libraries. This removes the need for extra application logic, or external utilities, to implement a cache.

CRC can be enabled by setting the database parameters CLIENT_RESULT_CACHE_SIZE and CLIENT_RESULT_CACHE_LAG, and then restarting the database, for example:

SQL> ALTER SYSTEM SET CLIENT_RESULT_CACHE_LAG = 3000 SCOPE=SPFILE;
SQL> ALTER SYSTEM SET CLIENT_RESULT_CACHE_SIZE = 64K SCOPE=SPFILE;
SQL> STARTUP FORCE

CRC can alternatively be configured in an oraaccess.xml <optclientfiles> or sqlnet.ora <optnetfiles> file on the Python host, see Client Configuration Parameters.

Tables can then be created, or altered, so repeated queries use CRC. This allows existing applications to use CRC without needing modification. For example:

SQL> CREATE TABLE cities (id number, name varchar2(40)) RESULT_CACHE (MODE FORCE);
SQL> ALTER TABLE locations RESULT_CACHE (MODE FORCE);

Alternatively, hints can be used in SQL statements. For example:

SELECT /*+ result_cache */ postal_code FROM locations