Configuration
This documentation is for an out-of-date version of Apache Flink. We recommend you use the latest stable version.

Configuration #

By default, the Table & SQL API is preconfigured for producing accurate results with acceptable performance.

Depending on the requirements of a table program, it might be necessary to adjust certain parameters for optimization. For example, unbounded streaming programs may need to ensure that the required state size is capped (see streaming concepts).

Overview #

In every table environment, the TableConfig offers options for configuring the current session.

For common or important configuration options, the TableConfig provides getters and setters methods with detailed inline documentation.

For more advanced configuration, users can directly access the underlying key-value map. The following sections list all available options that can be used to adjust Flink Table & SQL API programs.

Attention Because options are read at different point in time when performing operations, it is recommended to set configuration options early after instantiating a table environment.

// instantiate table environment
TableEnvironment tEnv = ...

// access flink configuration
Configuration configuration = tEnv.getConfig().getConfiguration();
// set low-level key-value options
configuration.setString("table.exec.mini-batch.enabled", "true");
configuration.setString("table.exec.mini-batch.allow-latency", "5 s");
configuration.setString("table.exec.mini-batch.size", "5000");

// instantiate table environment
val tEnv: TableEnvironment = ...

// access flink configuration
val configuration = tEnv.getConfig().getConfiguration()
// set low-level key-value options
configuration.setString("table.exec.mini-batch.enabled", "true")
configuration.setString("table.exec.mini-batch.allow-latency", "5 s")
configuration.setString("table.exec.mini-batch.size", "5000")

# instantiate table environment
t_env = ...

# access flink configuration
configuration = t_env.get_config().get_configuration()
# set low-level key-value options
configuration.set_string("table.exec.mini-batch.enabled", "true")
configuration.set_string("table.exec.mini-batch.allow-latency", "5 s")
configuration.set_string("table.exec.mini-batch.size", "5000")

Flink SQL> SET table.exec.mini-batch.enabled = true;
Flink SQL> SET table.exec.mini-batch.allow-latency = 5s;
Flink SQL> SET table.exec.mini-batch.size = 5000;

Execution Options #

The following options can be used to tune the performance of the query execution.

Key Default Type Description
table.exec.async-lookup.buffer-capacity

Batch Streaming		 100 Integer The max number of async i/o operation that the async lookup join can trigger.
table.exec.async-lookup.timeout

Batch Streaming		 3 min Duration The async timeout for the asynchronous operation to complete.
table.exec.disabled-operators

Batch		 (none) String Mainly for testing. A comma-separated list of operator names, each name represents a kind of disabled operator. Operators that can be disabled include "NestedLoopJoin", "ShuffleHashJoin", "BroadcastHashJoin", "SortMergeJoin", "HashAgg", "SortAgg". By default no operator is disabled.
table.exec.mini-batch.allow-latency

Streaming		 0 ms Duration The maximum latency can be used for MiniBatch to buffer input records. MiniBatch is an optimization to buffer input records to reduce state access. MiniBatch is triggered with the allowed latency interval and when the maximum number of buffered records reached. NOTE: If table.exec.mini-batch.enabled is set true, its value must be greater than zero.
table.exec.mini-batch.enabled

Streaming		 false Boolean Specifies whether to enable MiniBatch optimization. MiniBatch is an optimization to buffer input records to reduce state access. This is disabled by default. To enable this, users should set this config to true. NOTE: If mini-batch is enabled, 'table.exec.mini-batch.allow-latency' and 'table.exec.mini-batch.size' must be set.
table.exec.mini-batch.size

Streaming		 -1 Long The maximum number of input records can be buffered for MiniBatch. MiniBatch is an optimization to buffer input records to reduce state access. MiniBatch is triggered with the allowed latency interval and when the maximum number of buffered records reached. NOTE: MiniBatch only works for non-windowed aggregations currently. If table.exec.mini-batch.enabled is set true, its value must be positive.
table.exec.resource.default-parallelism

Batch Streaming		 -1 Integer Sets default parallelism for all operators (such as aggregate, join, filter) to run with parallel instances. This config has a higher priority than parallelism of StreamExecutionEnvironment (actually, this config overrides the parallelism of StreamExecutionEnvironment). A value of -1 indicates that no default parallelism is set, then it will fallback to use the parallelism of StreamExecutionEnvironment.
table.exec.shuffle-mode

Batch		 "ALL_EDGES_BLOCKING" String Sets exec shuffle mode.
Accepted values are:
  • ALL_EDGES_BLOCKING: All edges will use blocking shuffle.
  • FORWARD_EDGES_PIPELINED: Forward edges will use pipelined shuffle, others blocking.
  • POINTWISE_EDGES_PIPELINED: Pointwise edges will use pipelined shuffle, others blocking. Pointwise edges include forward and rescale edges.
  • ALL_EDGES_PIPELINED: All edges will use pipelined shuffle.
  • batch: the same as ALL_EDGES_BLOCKING. Deprecated.
  • pipelined: the same as ALL_EDGES_PIPELINED. Deprecated.
Note: Blocking shuffle means data will be fully produced before sent to consumer tasks. Pipelined shuffle means data will be sent to consumer tasks once produced.
table.exec.sink.not-null-enforcer

Batch Streaming		 ERROR

Enum

Possible values: [ERROR, DROP]		 The NOT NULL column constraint on a table enforces that null values can't be inserted into the table. Flink supports 'error' (default) and 'drop' enforcement behavior. By default, Flink will check values and throw runtime exception when null values writing into NOT NULL columns. Users can change the behavior to 'drop' to silently drop such records without throwing exception.
table.exec.sink.upsert-materialize

Streaming		 AUTO

Enum

Possible values: [NONE, AUTO, FORCE]		 Because of the disorder of ChangeLog data caused by Shuffle in distributed system, the data received by Sink may not be the order of global upsert. So add upsert materialize operator before upsert sink. It receives the upstream changelog records and generate an upsert view for the downstream.
By default, the materialize operator will be added when a distributed disorder occurs on unique keys. You can also choose no materialization(NONE) or force materialization(FORCE).
table.exec.sort.async-merge-enabled

Batch		 true Boolean Whether to asynchronously merge sorted spill files.
table.exec.sort.default-limit

Batch		 -1 Integer Default limit when user don't set a limit after order by. -1 indicates that this configuration is ignored.
table.exec.sort.max-num-file-handles

Batch		 128 Integer The maximal fan-in for external merge sort. It limits the number of file handles per operator. If it is too small, may cause intermediate merging. But if it is too large, it will cause too many files opened at the same time, consume memory and lead to random reading.
table.exec.source.cdc-events-duplicate

Streaming		 false Boolean Indicates whether the CDC (Change Data Capture) sources in the job will produce duplicate change events that requires the framework to deduplicate and get consistent result. CDC source refers to the source that produces full change events, including INSERT/UPDATE_BEFORE/UPDATE_AFTER/DELETE, for example Kafka source with Debezium format. The value of this configuration is false by default.

However, it's a common case that there are duplicate change events. Because usually the CDC tools (e.g. Debezium) work in at-least-once delivery when failover happens. Thus, in the abnormal situations Debezium may deliver duplicate change events to Kafka and Flink will get the duplicate events. This may cause Flink query to get wrong results or unexpected exceptions.

Therefore, it is recommended to turn on this configuration if your CDC tool is at-least-once delivery. Enabling this configuration requires to define PRIMARY KEY on the CDC sources. The primary key will be used to deduplicate change events and generate normalized changelog stream at the cost of an additional stateful operator.
table.exec.source.idle-timeout

Streaming		 0 ms Duration When a source do not receive any elements for the timeout time, it will be marked as temporarily idle. This allows downstream tasks to advance their watermarks without the need to wait for watermarks from this source while it is idle. Default value is 0, which means detecting source idleness is not enabled.
table.exec.spill-compression.block-size

Batch		 64 kb MemorySize The memory size used to do compress when spilling data. The larger the memory, the higher the compression ratio, but more memory resource will be consumed by the job.
table.exec.spill-compression.enabled

Batch		 true Boolean Whether to compress spilled data. Currently we only support compress spilled data for sort and hash-agg and hash-join operators.
table.exec.state.ttl

Streaming		 0 ms Duration Specifies a minimum time interval for how long idle state (i.e. state which was not updated), will be retained. State will never be cleared until it was idle for less than the minimum time, and will be cleared at some time after it was idle. Default is never clean-up the state. NOTE: Cleaning up state requires additional overhead for bookkeeping. Default value is 0, which means that it will never clean up state.
table.exec.window-agg.buffer-size-limit

Batch		 100000 Integer Sets the window elements buffer size limit used in group window agg operator.

Optimizer Options #

The following options can be used to adjust the behavior of the query optimizer to get a better execution plan.

Key Default Type Description
table.optimizer.agg-phase-strategy

Batch Streaming		 "AUTO" String Strategy for aggregate phase. Only AUTO, TWO_PHASE or ONE_PHASE can be set. AUTO: No special enforcer for aggregate stage. Whether to choose two stage aggregate or one stage aggregate depends on cost. TWO_PHASE: Enforce to use two stage aggregate which has localAggregate and globalAggregate. Note that if aggregate call does not support optimize into two phase, we will still use one stage aggregate. ONE_PHASE: Enforce to use one stage aggregate which only has CompleteGlobalAggregate.
table.optimizer.distinct-agg.split.bucket-num

Streaming		 1024 Integer Configure the number of buckets when splitting distinct aggregation. The number is used in the first level aggregation to calculate a bucket key 'hash_code(distinct_key) % BUCKET_NUM' which is used as an additional group key after splitting.
table.optimizer.distinct-agg.split.enabled

Streaming		 false Boolean Tells the optimizer whether to split distinct aggregation (e.g. COUNT(DISTINCT col), SUM(DISTINCT col)) into two level. The first aggregation is shuffled by an additional key which is calculated using the hashcode of distinct_key and number of buckets. This optimization is very useful when there is data skew in distinct aggregation and gives the ability to scale-up the job. Default is false.
table.optimizer.join-reorder-enabled

Batch Streaming		 false Boolean Enables join reorder in optimizer. Default is disabled.
table.optimizer.join.broadcast-threshold

Batch		 1048576 Long Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when performing a join. By setting this value to -1 to disable broadcasting.
table.optimizer.multiple-input-enabled

Batch		 true Boolean When it is true, the optimizer will merge the operators with pipelined shuffling into a multiple input operator to reduce shuffling and improve performance. Default value is true.
table.optimizer.reuse-source-enabled

Batch Streaming		 true Boolean When it is true, the optimizer will try to find out duplicated table sources and reuse them. This works only when table.optimizer.reuse-sub-plan-enabled is true.
table.optimizer.reuse-sub-plan-enabled

Batch Streaming		 true Boolean When it is true, the optimizer will try to find out duplicated sub-plans and reuse them.
table.optimizer.source.predicate-pushdown-enabled

Batch Streaming		 true Boolean When it is true, the optimizer will push down predicates into the FilterableTableSource. Default value is true.

Table Options #

The following options can be used to adjust the behavior of the table planner.

Key Default Type Description
table.dml-sync

Batch Streaming		 false Boolean Specifies if the DML job (i.e. the insert operation) is executed asynchronously or synchronously. By default, the execution is async, so you can submit multiple DML jobs at the same time. If set this option to true, the insert operation will wait for the job to finish.
table.dynamic-table-options.enabled

Batch Streaming		 false Boolean Enable or disable the OPTIONS hint used to specify table options dynamically, if disabled, an exception would be thrown if any OPTIONS hint is specified
table.generated-code.max-length

Batch Streaming		 64000 Integer Specifies a threshold where generated code will be split into sub-function calls. Java has a maximum method length of 64 KB. This setting allows for finer granularity if necessary.
table.local-time-zone

Batch Streaming		 "default" String The local time zone defines current session time zone id. It is used when converting to/from <code>TIMESTAMP WITH LOCAL TIME ZONE</code>. Internally, timestamps with local time zone are always represented in the UTC time zone. However, when converting to data types that don't include a time zone (e.g. TIMESTAMP, TIME, or simply STRING), the session time zone is used during conversion. The input of option is either a full name such as "America/Los_Angeles", or a custom timezone id such as "GMT-08:00".
table.planner

Batch Streaming		 BLINK

Enum

Possible values: [BLINK, OLD]		 Use either 'blink' planner or 'old' planner. Default is blink planner. For TableEnvironment, this option is used to construct a TableEnvironment, but this option can't be changed after that. However, there is no such limitation for SQL Client. Note: The old planner will be removed in Flink 1.14, so this option will become obsolete.
table.sql-dialect

Batch Streaming		 "default" String The SQL dialect defines how to parse a SQL query. A different SQL dialect may support different SQL grammar. Currently supported dialects are: default and hive

SQL Client Options #

The following options can be used to adjust the behavior of the sql client.

Key Default Type Description
sql-client.execution.max-table-result.rows

Batch Streaming		 1000000 Integer The number of rows to cache when in the table mode. If the number of rows exceeds the specified value, it retries the row in the FIFO style.
sql-client.execution.result-mode

Batch Streaming		 TABLE

Enum

Possible values: [TABLE, CHANGELOG, TABLEAU]		 Determine the mode when display the query result. The available values are ['table', 'tableau', 'changelog']. The 'table' mode materializes results in memory and visualizes them in a regular, paginated table representation. The 'changelog' mode does not materialize results and visualizes the result stream that is produced by a continuous query. The 'tableau' mode is more like a traditional way which will display the results in the screen directly with a tableau format.
sql-client.verbose

Batch Streaming		 false Boolean Determine whether to output the verbose output to the console. If set the option true, it will print the exception stack. Otherwise, it only output the cause.