This section provides information about manipulating data and concurrent access in Greenplum Database.
This topic includes the following subtopics:
Parent topic: Greenplum Database Administrator Guide
Greenplum Database and PostgreSQL do not use locks for concurrency control. They maintain data consistency using a multiversion model, Multiversion Concurrency Control (MVCC). MVCC achieves transaction isolation for each database session, and each query transaction sees a snapshot of data. This ensures the transaction sees consistent data that is not affected by other concurrent transactions.
Because MVCC does not use explicit locks for concurrency control, lock contention is minimized and Greenplum Database maintains reasonable performance in multiuser environments. Locks acquired for querying (reading) data do not conflict with locks acquired for writing data.
Greenplum Database provides multiple lock modes to control concurrent access to data in tables. Most Greenplum Database SQL commands automatically acquire the appropriate locks to ensure that referenced tables are not dropped or modified in incompatible ways while a command runs. For applications that cannot adapt easily to MVCC behavior, you can use the
LOCK command to acquire explicit locks. However, proper use of MVCC generally provides better performance.
|Lock Mode||Associated SQL Commands||Conflicts With|
||EXCLUSIVE, ACCESS EXCLUSIVE|
||SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, ACCESS EXCLUSIVE|
|SHARE UPDATE EXCLUSIVE||
||SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, ACCESS EXCLUSIVE|
||ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE ROW EXCLUSIVE, EXCLUSIVE, ACCESS EXCLUSIVE|
|SHARE ROW EXCLUSIVE||ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, ACCESS EXCLUSIVE|
||ROW SHARE, ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, ACCESS EXCLUSIVE|
||ACCESS SHARE, ROW SHARE, ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, ACCESS EXCLUSIVE|
By default Greenplum Database acquires the more restrictive
EXCLUSIVElock (rather than
ROW EXCLUSIVEin PostgreSQL) for
SELECT...FOR UPDATEoperations on heap tables. When the Global Deadlock Detector is enabled the lock mode for
DELETEoperations on heap tables is
ROW EXCLUSIVE. See Global Deadlock Detector. Greenplum always holds a table-level lock with
INSERT command to create rows in a table. This command requires the table name and a value for each column in the table; you may optionally specify the column names in any order. If you do not specify column names, list the data values in the order of the columns in the table, separated by commas.
For example, to specify the column names and the values to insert:
INSERT INTO products (name, price, product_no) VALUES ('Cheese', 9.99, 1);
To specify only the values to insert:
INSERT INTO products VALUES (1, 'Cheese', 9.99);
Usually, the data values are literals (constants), but you can also use scalar expressions. For example:
INSERT INTO films SELECT * FROM tmp_films WHERE date_prod < '2016-05-07';
You can insert multiple rows in a single command. For example:
INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99), (2, 'Bread', 1.99), (3, 'Milk', 2.99);
To insert data into a partitioned table, you specify the root partitioned table, the table created with the
CREATE TABLE command. You also can specify a leaf child table of the partitioned table in an
INSERT command. An error is returned if the data is not valid for the specified leaf child table. Specifying a child table that is not a leaf child table in the
INSERT command is not supported.
To insert large amounts of data, use external tables or the
COPY command. These load mechanisms are more efficient than
INSERT for inserting large quantities of rows. See Loading and Unloading Data for more information about bulk data loading.
The storage model of append-optimized tables is optimized for bulk data loading. Greenplum does not recommend single row
INSERT statements for append-optimized tables. For append-optimized tables, Greenplum Database supports a maximum of 127 concurrent
INSERT transactions into a single append-optimized table.
UPDATE command updates rows in a table. You can update all rows, a subset of all rows, or individual rows in a table. You can update each column separately without affecting other columns.
To perform an update, you need:
For example, the following command updates all products that have a price of 5 to have a price of 10:
UPDATE products SET price = 10 WHERE price = 5;
UPDATE in Greenplum Database has the following restrictions:
VOLATILEfunctions in an
DELETE command deletes rows from a table. Specify a
WHERE clause to delete rows that match certain criteria. If you do not specify a
WHERE clause, all rows in the table are deleted. The result is a valid, but empty, table. For example, to remove all rows from the products table that have a price of 10:
DELETE FROM products WHERE price = 10;
To delete all rows from a table:
DELETE FROM products;
DELETE in Greenplum Database has similar restrictions to using
VOLATILEfunctions in an
TRUNCATE command to quickly remove all rows in a table. For example:
This command empties a table of all rows in one operation. Note that
TRUNCATE does not scan the table, therefore it does not process inherited child tables or
ON DELETE rewrite rules. The command truncates only rows in the named table.
Transactions allow you to bundle multiple SQL statements in one all-or-nothing operation.
The following are the Greenplum Database SQL transaction commands:
START TRANSACTIONstarts a transaction block.
COMMITcommits the results of a transaction.
ROLLBACKabandons a transaction without making any changes.
SAVEPOINTmarks a place in a transaction and enables partial rollback. You can roll back commands run after a savepoint while maintaining commands run before the savepoint.
ROLLBACK TO SAVEPOINTrolls back a transaction to a savepoint.
RELEASE SAVEPOINTdestroys a savepoint within a transaction.
Greenplum Database accepts the standard SQL transaction levels as follows:
READ COMMITTEDbehave like the standard
The following information describes the behavior of the Greenplum transaction levels.
Greenplum Database does not allow any command to see an uncommitted update in another concurrent transaction, so
READ UNCOMMITTED behaves the same as
READ COMMITTED provides fast, simple, partial transaction isolation.
DELETE commands operate on a snapshot of the database taken when the query started.
SELECT queries in the same transaction can see different data if other concurrent transactions commit changes between the successive queries.
DELETE commands find only rows committed before the commands started.
READ COMMITTED transaction isolation allows concurrent transactions to modify or lock a row before
DELETE find the row.
READ COMMITTED transaction isolation may be inadequate for applications that perform complex queries and updates and require a consistent view of the database.
SERIALIZABLE transaction isolation, as defined by the SQL standard, ensures that transactions that run concurrently produce the same results as if they were run one after another. If you specify
SERIALIZABLE Greenplum Database falls back to
REPEATABLE READ transactions prevent dirty reads, non-repeatable reads, and phantom reads without expensive locking, but Greenplum Database does not detect all serializability interactions that can occur during concurrent transaction execution. Concurrent transactions should be examined to identify interactions that are not prevented by disallowing concurrent updates of the same data. You can prevent these interactions by using explicit table locks or by requiring the conflicting transactions to update a dummy row introduced to represent the conflict.
REPEATABLE READ transactions, a
SELECTcommands within a single transaction always see the same data.
SELECT FOR UPDATE, and
SELECT FOR SHAREcommands find only rows committed before the command started. If a concurrent transaction has updated, deleted, or locked a target row, the
REPEATABLE READtransaction waits for the concurrent transaction to commit or roll back the change. If the concurrent transaction commits the change, the
REPEATABLE READtransaction rolls back. If the concurrent transaction rolls back its change, the
REPEATABLE READtransaction can commit its changes.
The default transaction isolation level in Greenplum Database is
READ COMMITTED. To change the isolation level for a transaction, declare the isolation level when you
BEGIN the transaction or use the
SET TRANSACTION command after the transaction starts.
The Greenplum Database Global Deadlock Detector background worker process collects lock information on all segments and uses a directed algorithm to detect the existence of local and global deadlocks. This algorithm allows Greenplum Database to relax concurrent update and delete restrictions on heap tables. (Greenplum Database still employs table-level locking on AO/CO tables, restricting concurrent
SELECT...FOR lock_strength operations.)
By default, the Global Deadlock Detector is deactivated and Greenplum Database runs the concurrent
DELETE operations on a heap table serially. You can activate these concurrent updates and have the Global Deadlock Detector determine when a deadlock exists by setting the server configuration parameter
When the Global Deadlock Detector is enabled, the background worker process is automatically started on the master host when you start Greenplum Database. You configure the interval at which the Global Deadlock Detector collects and analyzes lock waiting data via the gp_global_deadlock_detector_period server configuration parameter.
If the Global Deadlock Detector determines that deadlock exists, it breaks the deadlock by cancelling one or more backend processes associated with the youngest transaction(s) involved.
When the Global Deadlock Detector determines a deadlock exists for the following types of transactions, only one of the transactions will succeed. The other transactions will fail with an error indicating that concurrent updates to the same row is not allowed.
Greenplum Database uses the interval specified in the deadlock_timeout server configuration parameter for local deadlock detection. Because the local and global deadlock detection algorithms differ, the cancelled process(es) may differ depending upon which detector (local or global) Greenplum Database triggers first.
If the lock_timeout server configuration parameter is turned on and set to a value smaller than
gp_global_deadlock_detector_period, Greenplum Database will cancel a statement before it would ever trigger a deadlock check in that session.
To view lock waiting information for all segments, run the
gp_dist_wait_status() user-defined function. You can use the output of this function to determine which transactions are waiting on locks, which transactions are holding locks, the lock types and mode, the waiter and holder session identifiers, and which segments are running the transactions. Sample output of the
gp_dist_wait_status() function follows:
SELECT * FROM pg_catalog.gp_dist_wait_status(); -[ RECORD 1 ]----+-------------- segid | 0 waiter_dxid | 11 holder_dxid | 12 holdTillEndXact | t waiter_lpid | 31249 holder_lpid | 31458 waiter_lockmode | ShareLock waiter_locktype | transactionid waiter_sessionid | 8 holder_sessionid | 9 -[ RECORD 2 ]----+-------------- segid | 1 waiter_dxid | 12 holder_dxid | 11 holdTillEndXact | t waiter_lpid | 31467 holder_lpid | 31250 waiter_lockmode | ShareLock waiter_locktype | transactionid waiter_sessionid | 9 holder_sessionid | 8
When it cancels a transaction to break a deadlock, the Global Deadlock Detector reports the following error message:
ERROR: canceling statement due to user request: "cancelled by global deadlock detector"
The Global Deadlock Detector can manage concurrent updates for these types of
DELETE commands on heap tables:
UPDATE of a single table. Update a non-distribution key with the Postgres Planner. The command does not contain a
FROM clause, or a sub-query in the
UPDATE t SET c2 = c2 + 1 WHERE c1 > 10;
DELETE of a single table. The command does not contain a sub-query in the
DELETE FROM t WHERE c1 > 10;
UPDATE. For the Postgres Planner, the
UPDATE command updates a distribution key.
UPDATE t SET c = c + 1; -- c is a distribution key
For GPORCA, the
UPDATE command updates a distribution key or references a distribution key.
UPDATE t SET b = b + 1 WHERE c = 10; -- c is a distribution key
UPDATE command includes multiple table joins.
UPDATE t1 SET c = t1.c+1 FROM t2 WHERE t1.c = t2.c;
Or the command contains a sub-query in the
UPDATE t SET c = c + 1 WHERE c > ALL(SELECT * FROM t1);
DELETE. A complex
DELETE command is similar to a complex
UPDATE, and involves multiple table joins or a sub-query.
DELETE FROM t USING t1 WHERE t.c > t1.c;
The following table shows the concurrent
DELETE commands that are managed by the Global Deadlock Detector. For example, concurrent simple
UPDATE commands on the same table row are managed by the Global Deadlock Detector. For a concurrent complex
UPDATE and a simple
UPDATE, only one
UPDATE is performed, and an error is returned for the other
Deleted or updated data rows occupy physical space on disk even though new transactions cannot see them. Periodically running the
VACUUM command removes these expired rows. For example:
VACUUM command collects table-level statistics such as the number of rows and pages. Vacuum all tables after loading data, including append-optimized tables. For information about recommended routine vacuum operations, see Routine Vacuum and Analyze.
VACUUM FULL, and
VACUUM ANALYZEcommands should be used to maintain the data in a Greenplum database especially if updates and deletes are frequently performed on your database data. See the
VACUUMcommand in the Greenplum Database Reference Guide for information about using the command.
Greenplum Database can potentially run out of locks when a database operation accesses multiple tables in a single transaction. Backup and restore are examples of such operations.
When Greenplum Database runs out of locks, the error message that you may observe references a shared memory error:
... "WARNING","53200","out of shared memory",,,,,,"LOCK TABLE ... ... "ERROR","53200","out of shared memory",,"You might need to increase max_locks_per_transaction.",,,,"LOCK TABLE ...
“shared memory” in this context refers to the shared memory of the internal object: the lock slots. “Out of shared memory” does not refer to exhaustion of system- or Greenplum-level memory resources.
As the hint describes, consider increasing the
max_locks_per_transaction server configuration parameter when you encounter this error.