You can monitor a Greenplum Database system using a variety of tools included with the system or available as add-ons.

Observing the Greenplum Database system day-to-day performance helps administrators understand the system behavior, plan workflow, and troubleshoot problems. This chapter discusses tools for monitoring database performance and activity.

Also, be sure to review Recommended Monitoring and Maintenance Tasks for monitoring activities you can script to quickly detect problems in the system.

Parent topic: Managing a Greenplum System

Monitoring Database Activity and Performance

VMware Greenplum Command Center, an optional web-based interface, provides cluster status information, graphical administrative tools, real-time query monitoring, and historical cluster and query data. Download the Greenplum Command Center package from VMware Tanzu Network and view the documentation at the Greenplum Command Center Documentation web site.

Monitoring System State

As a Greenplum Database administrator, you must monitor the system for problem events such as a segment going down or running out of disk space on a segment host. The following topics describe how to monitor the health of a Greenplum Database system and examine certain state information for a Greenplum Database system.

Checking System State

A Greenplum Database system is comprised of multiple PostgreSQL instances (the master and segments) spanning multiple machines. To monitor a Greenplum Database system, you need to know information about the system as a whole, as well as status information of the individual instances. The gpstate utility provides status information about a Greenplum Database system.

Viewing Master and Segment Status and Configuration

The default gpstate action is to check segment instances and show a brief status of the valid and failed segments. For example, to see a quick status of your Greenplum Database system:

$ gpstate

To see more detailed information about your Greenplum Database array configuration, use gpstate with the -s option:

$ gpstate -s

Viewing Your Mirroring Configuration and Status

If you are using mirroring for data redundancy, you may want to see the list of mirror segment instances in the system, their current synchronization status, and the mirror to primary mapping. For example, to see the mirror segments in the system and their status:

$ gpstate -m

To see the primary to mirror segment mappings:

$ gpstate -c

To see the status of the standby master mirror:

$ gpstate -f

Checking Disk Space Usage

A database administrator's most important monitoring task is to make sure the file systems where the master and segment data directories reside do not grow to more than 70 percent full. A filled data disk will not result in data corruption, but it may prevent normal database activity from continuing. If the disk grows too full, it can cause the database server to shut down.

You can use the gp_disk_free external table in the gp_toolkit administrative schema to check for remaining free space (in kilobytes) on the segment host file systems. For example:

=# SELECT * FROM gp_toolkit.gp_disk_free 
   ORDER BY dfsegment;

Checking Sizing of Distributed Databases and Tables

The gp_toolkit administrative schema contains several views that you can use to determine the disk space usage for a distributed Greenplum Database database, schema, table, or index.

For a list of the available sizing views for checking database object sizes and disk space, see the Greenplum Database Reference Guide.

Viewing Disk Space Usage for a Database

To see the total size of a database (in bytes), use the gp_size_of_database view in the gp_toolkit administrative schema. For example:

=> SELECT * FROM gp_toolkit.gp_size_of_database 
   ORDER BY sodddatname;

Viewing Disk Space Usage for a Table

The gp_toolkit administrative schema contains several views for checking the size of a table. The table sizing views list the table by object ID (not by name). To check the size of a table by name, you must look up the relation name (relname) in the pg_class table. For example:

=> SELECT relname AS name, sotdsize AS size, sotdtoastsize 
   AS toast, sotdadditionalsize AS other 
   FROM gp_toolkit.gp_size_of_table_disk as sotd, pg_class 
   WHERE sotd.sotdoid=pg_class.oid ORDER BY relname;

For a list of the available table sizing views, see the Greenplum Database Reference Guide.

Viewing Disk Space Usage for Indexes

The gp_toolkit administrative schema contains a number of views for checking index sizes. To see the total size of all index(es) on a table, use the gp_size_of_all_table_indexes view. To see the size of a particular index, use the gp_size_of_index view. The index sizing views list tables and indexes by object ID (not by name). To check the size of an index by name, you must look up the relation name (relname) in the pg_class table. For example:

=> SELECT soisize, relname as indexname
   FROM pg_class, gp_toolkit.gp_size_of_index
   WHERE pg_class.oid=gp_size_of_index.soioid 
   AND pg_class.relkind='i';

Checking for Data Distribution Skew

All tables in Greenplum Database are distributed, meaning their data is divided across all of the segments in the system. Unevenly distributed data may diminish query processing performance. A table's distribution policy, set at table creation time, determines how the table's rows are distributed. For information about choosing the table distribution policy, see the following topics:

The gp_toolkit administrative schema also contains a number of views for checking data distribution skew on a table. For information about how to check for uneven data distribution, see the Greenplum Database Reference Guide.

Viewing a Table's Distribution Key

To see the columns used as the data distribution key for a table, you can use the \d+ meta-command in psql to examine the definition of a table. For example:

=# `\d+ sales
`                Table "retail.sales"
 Column      |     Type     | Modifiers | Description
-------------+--------------+-----------+-------------
 sale_id     | integer      |           |
 amt         | float        |           |
 date        | date         |           |
Has OIDs: no
Distributed by: (sale_id)

When you create a replicated table, Greenplum Database stores all rows in the table on every segment. Replicated tables have no distribution key. Where the \d+ meta-command reports the distribution key for a normally distributed table, it shows Distributed Replicated for a replicated table.

Viewing Data Distribution

To see the data distribution of a table's rows (the number of rows on each segment), you can run a query such as:

=# SELECT gp_segment_id, count(*) 
   FROM <table_name> GROUP BY gp_segment_id;

A table is considered to have a balanced distribution if all segments have roughly the same number of rows.

Note

If you run this query on a replicated table, it fails because Greenplum Database does not permit user queries to reference the system column gp_segment_id (or the system columns ctid, cmin, cmax, xmin, and xmax) in replicated tables. Because every segment has all of the tables' rows, replicated tables are evenly distributed by definition.

Checking for Query Processing Skew

When a query is being processed, all segments should have equal workloads to ensure the best possible performance. If you identify a poorly-performing query, you may need to investigate further using the EXPLAIN command. For information about using the EXPLAIN command and query profiling, see Query Profiling.

Query processing workload can be skewed if the table's data distribution policy and the query predicates are not well matched. To check for processing skew, you can run a query such as:

=# SELECT gp_segment_id, count(*) FROM <table_name>
   WHERE <column>='<value>' GROUP BY gp_segment_id;

This will show the number of rows returned by segment for the given WHERE predicate.

As noted in Viewing Data Distribution, this query will fail if you run it on a replicated table because you cannot reference the gp_segment_id system column in a query on a replicated table.

Avoiding an Extreme Skew Warning

You may receive the following warning message while running a query that performs a hash join operation:

Extreme skew in the innerside of Hashjoin

This occurs when the input to a hash join operator is skewed. It does not prevent the query from completing successfully. You can follow these steps to avoid skew in the plan:

  1. Ensure that all fact tables are analyzed.
  2. Verify that any populated temporary table used by the query is analyzed.
  3. View the EXPLAIN ANALYZE plan for the query and look for the following:
    • If there are scans with multi-column filters that are producing more rows than estimated, then set the gp_selectivity_damping_factor server configuration parameter to 2 or higher and retest the query.
    • If the skew occurs while joining a single fact table that is relatively small (less than 5000 rows), set the gp_segments_for_planner server configuration parameter to 1 and retest the query.
  4. Check whether the filters applied in the query match distribution keys of the base tables. If the filters and distribution keys are the same, consider redistributing some of the base tables with different distribution keys.
  5. Check the cardinality of the join keys. If they have low cardinality, try to rewrite the query with different joining columns or additional filters on the tables to reduce the number of rows. These changes could change the query semantics.

Checking for and Terminating Overflowed Backends

Subtransaction overflow arises when a Greenplum Database backend creates more than 64 subtransactions, resulting in a high lookup cost for visibility checks. This slows query performance, but even more so when it occurs in combination with long-running transactions, which result in still more lookups. Terminating suboverflowed backends and/or backends with long-running transactions can help prevent and alleviate performance problems.

Greenplum Database includes an extension -- gp_subtransaction_overflow -- and a view -- gp_suboverflowed_backend -- that is run over a user-defined function to help users query for suboverflowed backends. Users can use segment id and process id information reported in the view to terminate the offending backends, thereby preventing degradation of performance.

Follow these steps to identify and terminate overflowed backends.

  1. Create the extension:

    CREATE EXTENSION gp_subtransaction_overflow;

  2. Select all from the view the extension created:

    select * from gp_suboverflowed_backend`;

    This returns output similar to the following:

    segid |   pids    
-------+-----------
-1 | 
 0 | {1731513}
 1 | {1731514}
 2 | {1731515}
(4 rows)

  3. Connect to the database in utility mode and query pg_stat_activity to return the session id for the process id in the output for a segment. For example:

    select sess_id from pg_stat_activity where pid=1731513;

    sess_id 
---------
  10
(1 row)

  4. Terminate the session, which will terminate all associated backends on all segments:

    select pg_terminate_backend(pid) from pg_stat_activity where sess_id=10;

  5. Verify that there are no more suboverflowed backends:

    select * from gp_suboverflowed_backend`;

    segid |   pids    
-------+-----------
-1 | 
 0 |
 1 | 
 2 | 
(4 rows)

Logging Statements that Cause Overflowed Subtransactions

You can optionally set a Greenplum configuration parameter, gp_log_suboverflow_statement, to record SQL statements that cause overflowed subtransactions. When this parameter is active, statements that cause overflow are recorded in server logs on the master host and segment hosts with the text: Statement caused suboverflow: <statement>.

One way to find these statements is to query the gp_toolkit.gp_log_system table. For example, after activating the setting:

SET set gp_log_suboverflow_statement = ON;

you can find statements that caused overflow with a query such as:

SELECT DISTINCT logsegment, logmessage FROM gp_toolkit.gp_log_system
	WHERE logmessage LIKE 'Statement caused suboverflow%';

 logsegment |                          logmessage                          
------------+--------------------------------------------------------------
 seg0       | Statement caused suboverflow: INSERT INTO t_1352_1 VALUES(i)
 seg1       | Statement caused suboverflow: INSERT INTO t_1352_1 VALUES(i)
 seg2       | Statement caused suboverflow: INSERT INTO t_1352_1 VALUES(i)
(3 rows)

Viewing Metadata Information about Database Objects

Greenplum Database tracks various metadata information in its system catalogs about the objects stored in a database, such as tables, views, indexes and so on, as well as global objects such as roles and tablespaces.

Viewing the Last Operation Performed

You can use the system views pg_stat_operations and pg_stat_partition_operations to look up actions performed on an object, such as a table. For example, to see the actions performed on a table, such as when it was created and when it was last vacuumed and analyzed:

=> SELECT schemaname as schema, objname as table, 
   usename as role, actionname as action, 
   subtype as type, statime as time 
   FROM pg_stat_operations 
   WHERE objname='cust';
 schema | table | role | action  | type  | time
--------+-------+------+---------+-------+--------------------------
  sales | cust  | main | CREATE  | TABLE | 2016-02-09 18:10:07.867977-08
  sales | cust  | main | VACUUM  |       | 2016-02-10 13:32:39.068219-08
  sales | cust  | main | ANALYZE |       | 2016-02-25 16:07:01.157168-08
(3 rows)

Viewing the Definition of an Object

To see the definition of an object, such as a table or view, you can use the \d+ meta-command when working in psql. For example, to see the definition of a table:

=> \d+ <mytable>

Viewing Session Memory Usage Information

You can create and use the session_level_memory_consumption view that provides information about the current memory utilization for sessions that are running queries on Greenplum Database. The view contains session information and information such as the database that the session is connected to, the query that the session is currently running, and memory consumed by the session processes.

Creating the session_level_memory_consumption View

To create the session_state.session_level_memory_consumption view in a Greenplum Database, run the script CREATE EXTENSION gp_internal_tools; once for each database. For example, to install the view in the database testdb, use this command:

$ psql -d testdb -c "CREATE EXTENSION gp_internal_tools;"

The session_level_memory_consumption View

The session_state.session_level_memory_consumption view provides information about memory consumption and idle time for sessions that are running SQL queries.

When resource queue-based resource management is active, the column is_runaway indicates whether Greenplum Database considers the session a runaway session based on the vmem memory consumption of the session's queries. Under the resource queue-based resource management scheme, Greenplum Database considers the session a runaway when the queries consume an excessive amount of memory. The Greenplum Database server configuration parameter runaway_detector_activation_percent governs the conditions under which Greenplum Database considers a session a runaway session.

The is_runaway, runaway_vmem_mb, and runaway_command_cnt columns are not applicable when resource group-based resource management is active.

column type references description
datname name Name of the database that the session is connected to.
sess_id integer Session ID.
usename name Name of the session user.
query text Current SQL query that the session is running.
segid integer Segment ID.
vmem_mb integer Total vmem memory usage for the session in MB.
is_runaway boolean Session is marked as runaway on the segment.
qe_count integer Number of query processes for the session.
active_qe_count integer Number of active query processes for the session.
dirty_qe_count integer Number of query processes that have not yet released their memory. The value is -1 for sessions that are not running.
runaway_vmem_mb integer Amount of vmem memory that the session was consuming when it was marked as a runaway session.
runaway_command_cnt integer Command count for the session when it was marked as a runaway session.
idle_start timestamptz The last time a query process in this session became idle.

Viewing Query Workfile Usage Information

The Greenplum Database administrative schema gp_toolkit contains views that display information about Greenplum Database workfiles. Greenplum Database creates workfiles on disk if it does not have sufficient memory to run the query in memory. This information can be used for troubleshooting and tuning queries. The information in the views can also be used to specify the values for the Greenplum Database configuration parameters gp_workfile_limit_per_query and gp_workfile_limit_per_segment.

These are the views in the schema gp_toolkit:

  • The gp_workfile_entries view contains one row for each operator using disk space for workfiles on a segment at the current time.
  • The gp_workfile_usage_per_query view contains one row for each query using disk space for workfiles on a segment at the current time.
  • The gp_workfile_usage_per_segment view contains one row for each segment. Each row displays the total amount of disk space used for workfiles on the segment at the current time.

For information about using gp_toolkit, see Using gp_toolkit.

Viewing the Database Server Log Files

Every database instance in Greenplum Database (master and segments) runs a PostgreSQL database server with its own server log file. Log files are created in the log directory of the master and each segment data directory.

Log File Format

The server log files are written in comma-separated values (CSV) format. Some log entries will not have values for all log fields. For example, only log entries associated with a query worker process will have the slice_id populated. You can identify related log entries of a particular query by the query's session identifier (gp_session_id) and command identifier (gp_command_count).

The following fields are written to the log:

Number Field Name Data Type Description
1 event_time timestamp with time zone Time that the log entry was written to the log
2 user_name varchar(100) The database user name
3 database_name varchar(100) The database name
4 process_id varchar(10) The system process ID (prefixed with "p")
5 thread_id varchar(50) The thread count (prefixed with "th")
6 remote_host varchar(100) On the master, the hostname/address of the client machine. On the segment, the hostname/address of the master.
7 remote_port varchar(10) The segment or master port number
8 session_start_time timestamp with time zone Time session connection was opened
9 transaction_id int Top-level transaction ID on the master. This ID is the parent of any subtransactions.
10 gp_session_id text Session identifier number (prefixed with "con")
11 gp_command_count text The command number within a session (prefixed with "cmd")
12 gp_segment text The segment content identifier (prefixed with "seg" for primaries or "mir" for mirrors). The master always has a content ID of -1.
13 slice_id text The slice ID (portion of the query plan being executed)
14 distr_tranx_id text Distributed transaction ID
15 local_tranx_id text Local transaction ID
16 sub_tranx_id text Subtransaction ID
17 event_severity varchar(10) Values include: LOG, ERROR, FATAL, PANIC, DEBUG1, DEBUG2
18 sql_state_code varchar(10) SQL state code associated with the log message
19 event_message text Log or error message text
20 event_detail text Detail message text associated with an error or warning message
21 event_hint text Hint message text associated with an error or warning message
22 internal_query text The internally-generated query text
23 internal_query_pos int The cursor index into the internally-generated query text
24 event_context text The context in which this message gets generated
25 debug_query_string text User-supplied query string with full detail for debugging. This string can be modified for internal use.
26 error_cursor_pos int The cursor index into the query string
27 func_name text The function in which this message is generated
28 file_name text The internal code file where the message originated
29 file_line int The line of the code file where the message originated
30 stack_trace text Stack trace text associated with this message

Searching the Greenplum Server Log Files

Greenplum Database provides a utility called gplogfilter can search through a Greenplum Database log file for entries matching the specified criteria. By default, this utility searches through the Greenplum Database master log file in the default logging location. For example, to display the last three lines of each of the log files under the master directory:

$ gplogfilter -n 3

To search through all segment log files simultaneously, run gplogfilter through the gpssh utility. For example, to display the last three lines of each segment log file:

$ gpssh -f seg_host_file


=> source /usr/local/greenplum-db/greenplum_path.sh
=> gplogfilter -n 3 /gpdata/gp*/log/gpdb*.log

Using gp_toolkit

Use the Greenplum Database administrative schema gp_toolkit to query the system catalogs, log files, and operating environment for system status information. The gp_toolkit schema contains several views you can access using SQL commands. The gp_toolkit schema is accessible to all database users. Some objects require superuser permissions. Use a command similar to the following to add the gp_toolkit schema to your schema search path:

=> ALTER ROLE myrole SET search_path TO myschema,gp_toolkit;

For a description of the available administrative schema views and their usages, see the Greenplum Database Reference Guide.

SQL Standard Error Codes

The following table lists all the defined error codes. Some are not used, but are defined by the SQL standard. The error classes are also shown. For each error class there is a standard error code having the last three characters 000. This code is used only for error conditions that fall within the class but do not have any more-specific code assigned.

The PL/pgSQL condition name for each error code is the same as the phrase shown in the table, with underscores substituted for spaces. For example, code 22012, DIVISION BY ZERO, has condition name DIVISION_BY_ZERO. Condition names can be written in either upper or lower case.

Note

PL/pgSQL does not recognize warning, as opposed to error, condition names; those are classes 00, 01, and 02.

Error Code Meaning Constant
Class 00— Successful Completion
00000 SUCCESSFUL COMPLETION successful_completion
Class 01 — Warning
01000 WARNING warning
0100C DYNAMIC RESULT SETS RETURNED dynamic_result_sets_returned
01008 IMPLICIT ZERO BIT PADDING implicit_zero_bit_padding
01003 NULL VALUE ELIMINATED IN SET FUNCTION null_value_eliminated_in_set_function
01007 PRIVILEGE NOT GRANTED privilege_not_granted
01006 PRIVILEGE NOT REVOKED privilege_not_revoked
01004 STRING DATA RIGHT TRUNCATION string_data_right_truncation
01P01 DEPRECATED FEATURE deprecated_feature
Class 02 — No Data (this is also a warning class per the SQL standard)
02000 NO DATA no_data
02001 NO ADDITIONAL DYNAMIC RESULT SETS RETURNED no_additional_dynamic_result_sets_returned
Class 03 — SQL Statement Not Yet Complete
03000 SQL STATEMENT NOT YET COMPLETE sql_statement_not_yet_complete
Class 08 — Connection Exception
08000 CONNECTION EXCEPTION connection_exception
08003 CONNECTION DOES NOT EXIST connection_does_not_exist
08006 CONNECTION FAILURE connection_failure
08001 SQLCLIENT UNABLE TO ESTABLISH SQLCONNECTION sqlclient_unable_to_establish_sqlconnection
08004 SQLSERVER REJECTED ESTABLISHMENT OF SQLCONNECTION sqlserver_rejected_establishment_of_sqlconnection
08007 TRANSACTION RESOLUTION UNKNOWN transaction_resolution_unknown
08P01 PROTOCOL VIOLATION protocol_violation
Class 09 — Triggered Action Exception
09000 TRIGGERED ACTION EXCEPTION triggered_action_exception
Class 0A — Feature Not Supported
0A000 FEATURE NOT SUPPORTED feature_not_supported
Class 0B — Invalid Transaction Initiation
0B000 INVALID TRANSACTION INITIATION invalid_transaction_initiation
Class 0F — Locator Exception
0F000 LOCATOR EXCEPTION locator_exception
0F001 INVALID LOCATOR SPECIFICATION invalid_locator_specification
Class 0L — Invalid Grantor
0L000 INVALID GRANTOR invalid_grantor
0LP01 INVALID GRANT OPERATION invalid_grant_operation
Class 0P — Invalid Role Specification
0P000 INVALID ROLE SPECIFICATION invalid_role_specification
Class 21 — Cardinality Violation
21000 CARDINALITY VIOLATION cardinality_violation
Class 22 — Data Exception
22000 DATA EXCEPTION data_exception
2202E ARRAY SUBSCRIPT ERROR array_subscript_error
22021 CHARACTER NOT IN REPERTOIRE character_not_in_repertoire
22008 DATETIME FIELD OVERFLOW datetime_field_overflow
22012 DIVISION BY ZERO division_by_zero
22005 ERROR IN ASSIGNMENT error_in_assignment
2200B ESCAPE CHARACTER CONFLICT escape_character_conflict
22022 INDICATOR OVERFLOW indicator_overflow
22015 INTERVAL FIELD OVERFLOW interval_field_overflow
2201E INVALID ARGUMENT FOR LOGARITHM invalid_argument_for_logarithm
2201F INVALID ARGUMENT FOR POWER FUNCTION invalid_argument_for_power_function
2201G INVALID ARGUMENT FOR WIDTH BUCKET FUNCTION invalid_argument_for_width_bucket_function
22018 INVALID CHARACTER VALUE FOR CAST invalid_character_value_for_cast
22007 INVALID DATETIME FORMAT invalid_datetime_format
22019 INVALID ESCAPE CHARACTER invalid_escape_character
2200D INVALID ESCAPE OCTET invalid_escape_octet
22025 INVALID ESCAPE SEQUENCE invalid_escape_sequence
22P06 NONSTANDARD USE OF ESCAPE CHARACTER nonstandard_use_of_escape_character
22010 INVALID INDICATOR PARAMETER VALUE invalid_indicator_parameter_value
22020 INVALID LIMIT VALUE invalid_limit_value
22023 INVALID PARAMETER VALUE invalid_parameter_value
2201B INVALID REGULAR EXPRESSION invalid_regular_expression
22009 INVALID TIME ZONE DISPLACEMENT VALUE invalid_time_zone_displacement_value
2200C INVALID USE OF ESCAPE CHARACTER invalid_use_of_escape_character
2200G MOST SPECIFIC TYPE MISMATCH most_specific_type_mismatch
22004 NULL VALUE NOT ALLOWED null_value_not_allowed
22002 NULL VALUE NO INDICATOR PARAMETER null_value_no_indicator_parameter
22003 NUMERIC VALUE OUT OF RANGE numeric_value_out_of_range
22026 STRING DATA LENGTH MISMATCH string_data_length_mismatch
22001 STRING DATA RIGHT TRUNCATION string_data_right_truncation
22011 SUBSTRING ERROR substring_error
22027 TRIM ERROR trim_error
22024 UNTERMINATED C STRING unterminated_c_string
2200F ZERO LENGTH CHARACTER STRING zero_length_character_string
22P01 FLOATING POINT EXCEPTION floating_point_exception
22P02 INVALID TEXT REPRESENTATION invalid_text_representation
22P03 INVALID BINARY REPRESENTATION invalid_binary_representation
22P04 BAD COPY FILE FORMAT bad_copy_file_format
22P05 UNTRANSLATABLE CHARACTER untranslatable_character
Class 23 — Integrity Constraint Violation
23000 INTEGRITY CONSTRAINT VIOLATION integrity_constraint_violation
23001 RESTRICT VIOLATION restrict_violation
23502 NOT NULL VIOLATION not_null_violation
23503 FOREIGN KEY VIOLATION foreign_key_violation
23505 UNIQUE VIOLATION unique_violation
23514 CHECK VIOLATION check_violation
Class 24 — Invalid Cursor State
24000 INVALID CURSOR STATE invalid_cursor_state
Class 25 — Invalid Transaction State
25000 INVALID TRANSACTION STATE invalid_transaction_state
25001 ACTIVE SQL TRANSACTION active_sql_transaction
25002 BRANCH TRANSACTION ALREADY ACTIVE branch_transaction_already_active
25008 HELD CURSOR REQUIRES SAME ISOLATION LEVEL held_cursor_requires_same_isolation_level
25003 INAPPROPRIATE ACCESS MODE FOR BRANCH TRANSACTION inappropriate_access_mode_for_branch_transaction
25004 INAPPROPRIATE ISOLATION LEVEL FOR BRANCH TRANSACTION inappropriate_isolation_level_for_branch_transaction
25005 NO ACTIVE SQL TRANSACTION FOR BRANCH TRANSACTION no_active_sql_transaction_for_branch_transaction
25006 READ ONLY SQL TRANSACTION read_only_sql_transaction
25007 SCHEMA AND DATA STATEMENT MIXING NOT SUPPORTED schema_and_data_statement_mixing_not_supported
25P01 NO ACTIVE SQL TRANSACTION no_active_sql_transaction
25P02 IN FAILED SQL TRANSACTION in_failed_sql_transaction
Class 26 — Invalid SQL Statement Name
26000 INVALID SQL STATEMENT NAME invalid_sql_statement_name
Class 27 — Triggered Data Change Violation
27000 TRIGGERED DATA CHANGE VIOLATION triggered_data_change_violation
Class 28 — Invalid Authorization Specification
28000 INVALID AUTHORIZATION SPECIFICATION invalid_authorization_specification
Class 2B — Dependent Privilege Descriptors Still Exist
2B000 DEPENDENT PRIVILEGE DESCRIPTORS STILL EXIST dependent_privilege_descriptors_still_exist
2BP01 DEPENDENT OBJECTS STILL EXIST dependent_objects_still_exist
Class 2D — Invalid Transaction Termination
2D000 INVALID TRANSACTION TERMINATION invalid_transaction_termination
Class 2F — SQL Routine Exception
2F000 SQL ROUTINE EXCEPTION sql_routine_exception
2F005 FUNCTION EXECUTED NO RETURN STATEMENT function_executed_no_return_statement
2F002 MODIFYING SQL DATA NOT PERMITTED modifying_sql_data_not_permitted
2F003 PROHIBITED SQL STATEMENT ATTEMPTED prohibited_sql_statement_attempted
2F004 READING SQL DATA NOT PERMITTED reading_sql_data_not_permitted
Class 34 — Invalid Cursor Name
34000 INVALID CURSOR NAME invalid_cursor_name
Class 38 — External Routine Exception
38000 EXTERNAL ROUTINE EXCEPTION external_routine_exception
38001 CONTAINING SQL NOT PERMITTED containing_sql_not_permitted
38002 MODIFYING SQL DATA NOT PERMITTED modifying_sql_data_not_permitted
38003 PROHIBITED SQL STATEMENT ATTEMPTED prohibited_sql_statement_attempted
38004 READING SQL DATA NOT PERMITTED reading_sql_data_not_permitted
Class 39 — External Routine Invocation Exception
39000 EXTERNAL ROUTINE INVOCATION EXCEPTION external_routine_invocation_exception
39001 INVALID SQLSTATE RETURNED invalid_sqlstate_returned
39004 NULL VALUE NOT ALLOWED null_value_not_allowed
39P01 TRIGGER PROTOCOL VIOLATED trigger_protocol_violated
39P02 SRF PROTOCOL VIOLATED srf_protocol_violated
Class 3B — Savepoint Exception
3B000 SAVEPOINT EXCEPTION savepoint_exception
3B001 INVALID SAVEPOINT SPECIFICATION invalid_savepoint_specification
Class 3D — Invalid Catalog Name
3D000 INVALID CATALOG NAME invalid_catalog_name
Class 3F — Invalid Schema Name
3F000 INVALID SCHEMA NAME invalid_schema_name
Class 40 — Transaction Rollback
40000 TRANSACTION ROLLBACK transaction_rollback
40002 TRANSACTION INTEGRITY CONSTRAINT VIOLATION transaction_integrity_constraint_violation
40001 SERIALIZATION FAILURE serialization_failure
40003 STATEMENT COMPLETION UNKNOWN statement_completion_unknown
40P01 DEADLOCK DETECTED deadlock_detected
Class 42 — Syntax Error or Access Rule Violation
42000 SYNTAX ERROR OR ACCESS RULE VIOLATION syntax_error_or_access_rule_violation
42601 SYNTAX ERROR syntax_error
42501 INSUFFICIENT PRIVILEGE insufficient_privilege
42846 CANNOT COERCE cannot_coerce
42803 GROUPING ERROR grouping_error
42830 INVALID FOREIGN KEY invalid_foreign_key
42602 INVALID NAME invalid_name
42622 NAME TOO LONG name_too_long
42939 RESERVED NAME reserved_name
42804 DATATYPE MISMATCH datatype_mismatch
42P18 INDETERMINATE DATATYPE indeterminate_datatype
42809 WRONG OBJECT TYPE wrong_object_type
42703 UNDEFINED COLUMN undefined_column
42883 UNDEFINED FUNCTION undefined_function
42P01 UNDEFINED TABLE undefined_table
42P02 UNDEFINED PARAMETER undefined_parameter
42704 UNDEFINED OBJECT undefined_object
42701 DUPLICATE COLUMN duplicate_column
42P03 DUPLICATE CURSOR duplicate_cursor
42P04 DUPLICATE DATABASE duplicate_database
42723 DUPLICATE FUNCTION duplicate_function
42P05 DUPLICATE PREPARED STATEMENT duplicate_prepared_statement
42P06 DUPLICATE SCHEMA duplicate_schema
42P07 DUPLICATE TABLE duplicate_table
42712 DUPLICATE ALIAS duplicate_alias
42710 DUPLICATE OBJECT duplicate_object
42702 AMBIGUOUS COLUMN ambiguous_column
42725 AMBIGUOUS FUNCTION ambiguous_function
42P08 AMBIGUOUS PARAMETER ambiguous_parameter
42P09 AMBIGUOUS ALIAS ambiguous_alias
42P10 INVALID COLUMN REFERENCE invalid_column_reference
42611 INVALID COLUMN DEFINITION invalid_column_definition
42P11 INVALID CURSOR DEFINITION invalid_cursor_definition
42P12 INVALID DATABASE DEFINITION invalid_database_definition
42P13 INVALID FUNCTION DEFINITION invalid_function_definition
42P14 INVALID PREPARED STATEMENT DEFINITION invalid_prepared_statement_definition
42P15 INVALID SCHEMA DEFINITION invalid_schema_definition
42P16 INVALID TABLE DEFINITION invalid_table_definition
42P17 INVALID OBJECT DEFINITION invalid_object_definition
Class 44 — WITH CHECK OPTION Violation
44000 WITH CHECK OPTION VIOLATION with_check_option_violation
Class 53 — Insufficient Resources
53000 INSUFFICIENT RESOURCES insufficient_resources
53100 DISK FULL disk_full
53200 OUT OF MEMORY out_of_memory
53300 TOO MANY CONNECTIONS too_many_connections
Class 54 — Program Limit Exceeded
54000 PROGRAM LIMIT EXCEEDED program_limit_exceeded
54001 STATEMENT TOO COMPLEX statement_too_complex
54011 TOO MANY COLUMNS too_many_columns
54023 TOO MANY ARGUMENTS too_many_arguments
Class 55 — Object Not In Prerequisite State
55000 OBJECT NOT IN PREREQUISITE STATE object_not_in_prerequisite_state
55006 OBJECT IN USE object_in_use
55P02 CANT CHANGE RUNTIME PARAM cant_change_runtime_param
55P03 LOCK NOT AVAILABLE lock_not_available
Class 57 — Operator Intervention
57000 OPERATOR INTERVENTION operator_intervention
57014 QUERY CANCELED query_canceled
57P01 ADMIN SHUTDOWN admin_shutdown
57P02 CRASH SHUTDOWN crash_shutdown
57P03 CANNOT CONNECT NOW cannot_connect_now
Class 58 — System Error (errors external to Greenplum Database )
58030 IO ERROR io_error
58P01 UNDEFINED FILE undefined_file
58P02 DUPLICATE FILE duplicate_file
Class F0 — Configuration File Error
F0000 CONFIG FILE ERROR config_file_error
F0001 LOCK FILE EXISTS lock_file_exists
Class P0 — PL/pgSQL Error
P0000 PLPGSQL ERROR plpgsql_error
P0001 RAISE EXCEPTION raise_exception
P0002 NO DATA FOUND no_data_found
P0003 TOO MANY ROWS too_many_rows
Class XX — Internal Error
XX000 INTERNAL ERROR internal_error
XX001 DATA CORRUPTED data_corrupted
XX002 INDEX CORRUPTED index_corrupted
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