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

When instantiating a TableEnvironment, EnvironmentSettings can be used to pass the desired configuration for the current session, by passing a Configuration object to the EnvironmentSettings.

Additionally, 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.

Java

  1. // instantiate table environment
  2. Configuration configuration = new Configuration();
  3. // set low-level key-value options
  4. configuration.setString("table.exec.mini-batch.enabled", "true");
  5. configuration.setString("table.exec.mini-batch.allow-latency", "5 s");
  6. configuration.setString("table.exec.mini-batch.size", "5000");
  7. EnvironmentSettings settings = EnvironmentSettings.newInstance()
  8.         .inStreamingMode().withConfiguration(configuration).build();
  9. TableEnvironment tEnv = TableEnvironment.create(settings);
  11. // access flink configuration after table environment instantiation
  12. TableConfig tableConfig = tEnv.getConfig();
  13. // set low-level key-value options
  14. tableConfig.set("table.exec.mini-batch.enabled", "true");
  15. tableConfig.set("table.exec.mini-batch.allow-latency", "5 s");
  16. tableConfig.set("table.exec.mini-batch.size", "5000");

Scala

  1. // instantiate table environment
  2. val configuration = new Configuration;
  3. // set low-level key-value options
  4. configuration.setString("table.exec.mini-batch.enabled", "true")
  5. configuration.setString("table.exec.mini-batch.allow-latency", "5 s")
  6. configuration.setString("table.exec.mini-batch.size", "5000")
  7. val settings = EnvironmentSettings.newInstance
  8.   .inStreamingMode.withConfiguration(configuration).build
  9. val tEnv: TableEnvironment = TableEnvironment.create(settings)
  11. // access flink configuration after table environment instantiation
  12. val tableConfig = tEnv.getConfig()
  13. // set low-level key-value options
  14. tableConfig.set("table.exec.mini-batch.enabled", "true")
  15. tableConfig.set("table.exec.mini-batch.allow-latency", "5 s")
  16. tableConfig.set("table.exec.mini-batch.size", "5000")

Python

  1. # instantiate table environment
  2. configuration = Configuration()
  3. configuration.set("table.exec.mini-batch.enabled", "true")
  4. configuration.set("table.exec.mini-batch.allow-latency", "5 s")
  5. configuration.set("table.exec.mini-batch.size", "5000")
  6. settings = EnvironmentSettings.new_instance() \
  7. ...     .in_streaming_mode() \
  8. ...     .with_configuration(configuration) \
  9. ...     .build()
  11. t_env = TableEnvironment.create(settings)
  13. # access flink configuration after table environment instantiation
  14. table_config = t_env.get_config()
  15. # set low-level key-value options
  16. table_config.set("table.exec.mini-batch.enabled", "true")
  17. table_config.set("table.exec.mini-batch.allow-latency", "5 s")
  18. table_config.set("table.exec.mini-batch.size", "5000")

SQL CLI

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

Note: All of the following configuration options can also be set globally in Flink configuration file and can be later on overridden in the application, through EnvironmentSettings, before instantiating the TableEnvironment, or through the TableConfig of the TableEnvironment.

Execution Options

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

KeyDefaultTypeDescription
table.exec.async-lookup.buffer-capacity

Batch Streaming		100IntegerThe max number of async i/o operation that the async lookup join can trigger.
table.exec.async-lookup.output-mode

Batch Streaming		ORDERED

Enum

Output mode for asynchronous operations which will convert to {@see AsyncDataStream.OutputMode}, ORDERED by default. If set to ALLOWUNORDERED, will attempt to use {@see AsyncDataStream.OutputMode.UNORDERED} when it does not affect the correctness of the result, otherwise ORDERED will be still used.

Possible values:
  • “ORDERED”
  • “ALLOW_UNORDERED”
table.exec.async-lookup.timeout

Batch Streaming		3 minDurationThe async timeout for the asynchronous operation to complete.
table.exec.async-scalar.buffer-capacity

Streaming		10IntegerThe max number of async i/o operation that the async lookup join can trigger.
table.exec.async-scalar.max-attempts

Streaming		3IntegerThe max number of async retry attempts to make before task execution is failed.
table.exec.async-scalar.retry-delay

Streaming		100 msDurationThe delay to wait before trying again.
table.exec.async-scalar.retry-strategy

Streaming		FIXED_DELAY

Enum

Restart strategy which will be used, FIXED_DELAY by default.

Possible values:
  • “NO_RETRY”
  • “FIXED_DELAY”
table.exec.async-scalar.timeout

Streaming		3 minDurationThe async timeout for the asynchronous operation to complete.
table.exec.deduplicate.insert-update-after-sensitive-enabled

Streaming		trueBooleanSet whether the job (especially the sinks) is sensitive to INSERT messages and UPDATE_AFTER messages. If false, Flink may, sometimes (e.g. deduplication for last row), send UPDATE_AFTER instead of INSERT for the first row. If true, Flink will guarantee to send INSERT for the first row, in that case there will be additional overhead. Default is true.
table.exec.deduplicate.mini-batch.compact-changes-enabled

Streaming		falseBooleanSet whether to compact the changes sent downstream in row-time mini-batch. If true, Flink will compact changes and send only the latest change downstream. Note that if the downstream needs the details of versioned data, this optimization cannot be applied. If false, Flink will send all changes to downstream just like when the mini-batch is not enabled.
table.exec.disabled-operators

Batch		(none)StringMainly 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.interval-join.min-cleanup-interval

Streaming		0 msDurationSpecifies a minimum time interval for how long cleanup unmatched records in the interval join operator. Before Flink 1.18, the default value of this param was the half of interval duration. Note: Set this option greater than 0 will cause unmatched records in outer joins to be output later than watermark, leading to possible discarding of these records by downstream watermark-dependent operators, such as window operators. The default value is 0, which means it will clean up unmatched records immediately.
table.exec.legacy-cast-behaviour

Batch Streaming		DISABLED

Enum

Determines whether CAST will operate following the legacy behaviour or the new one that introduces various fixes and improvements.

Possible values:
  • “ENABLED”: CAST will operate following the legacy behaviour.
  • “DISABLED”: CAST will operate following the new correct behaviour.
table.exec.mini-batch.allow-latency

Streaming		0 msDurationThe 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		falseBooleanSpecifies 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		-1LongThe 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.operator-fusion-codegen.enabled

Batch Streaming		falseBooleanIf true, multiple physical operators will be compiled into a single operator by planner which can improve the performance.
table.exec.rank.topn-cache-size

Streaming		10000LongRank operators have a cache which caches partial state contents to reduce state access. Cache size is the number of records in each ranking task.
table.exec.resource.default-parallelism

Batch Streaming		-1IntegerSets 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.simplify-operator-name-enabled

Batch Streaming		trueBooleanWhen it is true, the optimizer will simplify the operator name with id and type of ExecNode and keep detail in description. Default value is true.
table.exec.sink.keyed-shuffle

Streaming		AUTO

Enum

In order to minimize the distributed disorder problem when writing data into table with primary keys that many users suffers. FLINK will auto add a keyed shuffle by default when the sink parallelism differs from upstream operator and sink parallelism is not 1. This works only when the upstream ensures the multi-records’ order on the primary key, if not, the added shuffle can not solve the problem (In this situation, a more proper way is to consider the deduplicate operation for the source firstly or use an upsert source with primary key definition which truly reflect the records evolution).
By default, the keyed shuffle will be added when the sink’s parallelism differs from upstream operator. You can set to no shuffle(NONE) or force shuffle(FORCE).

Possible values:
  • “NONE”
  • “AUTO”
  • “FORCE”
table.exec.sink.not-null-enforcer

Batch Streaming		ERROR

Enum

Determines how Flink enforces NOT NULL column constraints when inserting null values.

Possible values:
  • “ERROR”: Throw a runtime exception when writing null values into NOT NULL column.
  • “DROP”: Drop records silently if a null value would have to be inserted into a NOT NULL column.
table.exec.sink.rowtime-inserter

Streaming		ENABLED

Enum

Some sink implementations require a single rowtime attribute in the input that can be inserted into the underlying stream record. This option allows disabling the timestamp insertion and avoids errors around multiple time attributes being present in the query schema.

Possible values:
  • “ENABLED”: Insert a rowtime attribute (if available) into the underlying stream record. This requires at most one time attribute in the input for the sink.
  • “DISABLED”: Do not insert the rowtime attribute into the underlying stream record.
table.exec.sink.type-length-enforcer

Batch Streaming		IGNORE

Enum

Determines whether values for columns with CHAR(<length>)/VARCHAR(<length>)/BINARY(<length>)/VARBINARY(<length>) types will be trimmed or padded (only for CHAR(<length>)/BINARY(<length>)), so that their length will match the one defined by the length of their respective CHAR/VARCHAR/BINARY/VARBINARY column type.

Possible values:
  • “IGNORE”: Don’t apply any trimming and padding, and instead ignore the CHAR/VARCHAR/BINARY/VARBINARY length directive.
  • “TRIM_PAD”: Trim and pad string and binary values to match the length defined by the CHAR/VARCHAR/BINARY/VARBINARY length.
table.exec.sink.upsert-materialize

Streaming		AUTO

Enum

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).

Possible values:
  • “NONE”
  • “AUTO”
  • “FORCE”
table.exec.sort.async-merge-enabled

Batch		trueBooleanWhether to asynchronously merge sorted spill files.
table.exec.sort.default-limit

Batch		-1IntegerDefault 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		128IntegerThe 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		falseBooleanIndicates 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 msDurationWhen 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 kbMemorySizeThe 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		trueBooleanWhether 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 msDurationSpecifies 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.uid.format

Streaming		“<id><transformation>”StringDefines the format pattern for generating the UID of an ExecNode streaming transformation. The pattern can be defined globally or per-ExecNode in the compiled plan. Supported arguments are: <id> (from static counter), <type> (e.g. ‘stream-exec-sink’), <version>, and <transformation> (e.g. ‘constraint-validator’ for a sink). In Flink 1.15.x the pattern was wrongly defined as ‘<id><type><version>_<transformation>’ which would prevent migrations in the future.
table.exec.uid.generation

Streaming		PLAN_ONLY

Enum

In order to remap state to operators during a restore, it is required that the pipeline’s streaming transformations get a UID assigned.
The planner can generate and assign explicit UIDs. If no UIDs have been set by the planner, the UIDs will be auto-generated by lower layers that can take the complete topology into account for uniqueness of the IDs. See the DataStream API for more information.
This configuration option is for experts only and the default should be sufficient for most use cases. By default, only pipelines created from a persisted compiled plan will get UIDs assigned explicitly. Thus, these pipelines can be arbitrarily moved around within the same topology without affecting the stable UIDs.

Possible values:
  • “PLAN_ONLY”: Sets UIDs on streaming transformations if and only if the pipeline definition comes from a compiled plan. Pipelines that have been constructed in the API without a compilation step will not set an explicit UID as it might not be stable across multiple translations.
  • “ALWAYS”: Always sets UIDs on streaming transformations. This strategy is for experts only! Pipelines that have been constructed in the API without a compilation step might not be able to be restored properly. The UID generation depends on previously declared pipelines (potentially across jobs if the same JVM is used). Thus, a stable environment must be ensured. Pipeline definitions that come from a compiled plan are safe to use.
  • “DISABLED”: No explicit UIDs will be set.
table.exec.window-agg.buffer-size-limit

Batch		100000IntegerSets 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.

KeyDefaultTypeDescription
table.optimizer.agg-phase-strategy

Batch Streaming		“AUTO”StringStrategy 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.bushy-join-reorder-threshold

Batch Streaming		12IntegerThe maximum number of joined nodes allowed in the bushy join reorder algorithm, otherwise the left-deep join reorder algorithm will be used. The search space of bushy join reorder algorithm will increase with the increase of this threshold value, so this threshold is not recommended to be set too large. The default value is 12.
table.optimizer.distinct-agg.split.bucket-num

Streaming		1024IntegerConfigure 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		falseBooleanTells 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.dynamic-filtering.enabled

Batch Streaming		trueBooleanWhen it is true, the optimizer will try to push dynamic filtering into scan table source, the irrelevant partitions or input data will be filtered to reduce scan I/O in runtime.
table.optimizer.join-reorder-enabled

Batch Streaming		falseBooleanEnables join reorder in optimizer. Default is disabled.
table.optimizer.join.broadcast-threshold

Batch		1048576LongConfigures 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		trueBooleanWhen 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.non-deterministic-update.strategy

Streaming		IGNORE

Enum

When it is TRY_RESOLVE, the optimizer tries to resolve the correctness issue caused by ‘Non-Deterministic Updates’ (NDU) in a changelog pipeline. Changelog may contain kinds of message types: Insert (I), Delete (D), Update_Before (UB), Update_After (UA). There’s no NDU problem in an insert only changelog pipeline. For updates, there are three main NDU problems:
1. Non-deterministic functions, include scalar, table, aggregate functions, both builtin and custom ones.
2. LookupJoin on an evolving source
3. Cdc-source carries metadata fields which are system columns, not belongs to the entity data itself.

For the first step, the optimizer automatically enables the materialization for No.2(LookupJoin) if needed, and gives the detailed error message for No.1(Non-deterministic functions) and No.3(Cdc-source with metadata) which is relatively easier to solve by changing the SQL.
Default value is IGNORE, the optimizer does no changes.

Possible values:
  • “TRY_RESOLVE”
  • “IGNORE”
table.optimizer.reuse-source-enabled

Batch Streaming		trueBooleanWhen 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		trueBooleanWhen it is true, the optimizer will try to find out duplicated sub-plans and reuse them.
table.optimizer.runtime-filter.enabled

Batch		falseBooleanA flag to enable or disable the runtime filter. When it is true, the optimizer will try to inject a runtime filter for eligible join.
table.optimizer.runtime-filter.max-build-data-size

Batch		150 mbMemorySizeMax data volume threshold of the runtime filter build side. Estimated data volume needs to be under this value to try to inject runtime filter.
table.optimizer.runtime-filter.min-filter-ratio

Batch		0.5DoubleMin filter ratio threshold of the runtime filter. Estimated filter ratio needs to be over this value to try to inject runtime filter.
table.optimizer.runtime-filter.min-probe-data-size

Batch		10 gbMemorySizeMin data volume threshold of the runtime filter probe side. Estimated data volume needs to be over this value to try to inject runtime filter.This value should be larger than table.optimizer.runtime-filter.max-build-data-size.
table.optimizer.source.aggregate-pushdown-enabled

Batch		trueBooleanWhen it is true, the optimizer will push down the local aggregates into the TableSource which implements SupportsAggregatePushDown.
table.optimizer.source.predicate-pushdown-enabled

Batch Streaming		trueBooleanWhen it is true, the optimizer will push down predicates into the FilterableTableSource. Default value is true.
table.optimizer.source.report-statistics-enabled

Batch Streaming		trueBooleanWhen it is true, the optimizer will collect and use the statistics from source connectors if the source extends from SupportsStatisticReport and the statistics from catalog is UNKNOWN.Default value is true.
table.optimizer.sql2rel.project-merge.enabled

Batch Streaming		falseBooleanIf set to true, it will merge projects when converting SqlNode to RelNode.
Note: it is not recommended to turn on unless you are aware of possible side effects, such as causing the output of certain non-deterministic expressions to not meet expectations(see FLINK-20887).

Table Options

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

KeyDefaultTypeDescription
table.builtin-catalog-name

Batch Streaming		“default_catalog”StringThe name of the initial catalog to be created when instantiating a TableEnvironment.
table.builtin-database-name

Batch Streaming		“default_database”StringThe name of the default database in the initial catalog to be created when instantiating TableEnvironment.
table.catalog-modification.listeners

Batch Streaming		(none)List<String>A (semicolon-separated) list of factories that creates listener for catalog modification which will be notified in catalog manager after it performs database and table ddl operations successfully.
table.column-expansion-strategy

Batch Streaming

List<Enum>

Configures the default expansion behavior of ‘SELECT *’. By default, all top-level columns of the table’s schema are selected and nested fields are retained.

Possible values:
  • “EXCLUDE_ALIASED_VIRTUAL_METADATA_COLUMNS”: Excludes virtual metadata columns that reference a metadata key via an alias. For example, a column declared as ‘c METADATA VIRTUAL FROM k’ is not selected by default if the strategy is applied.
  • “EXCLUDE_DEFAULT_VIRTUAL_METADATA_COLUMNS”: Excludes virtual metadata columns that directly reference a metadata key. For example, a column declared as ‘k METADATA VIRTUAL’ is not selected by default if the strategy is applied.
table.display.max-column-width

Batch Streaming		30IntegerWhen printing the query results to the client console, this parameter determines the number of characters shown on screen before truncating. This only applies to columns with variable-length types (e.g. CHAR, VARCHAR, STRING) in the streaming mode. Fixed-length types are printed in the batch mode using a deterministic column width.
table.dml-sync

Batch Streaming		falseBooleanSpecifies 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		trueBooleanEnable 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		4000IntegerSpecifies 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. Default value is 4000 instead of 64KB as by default JIT refuses to work on methods with more than 8K byte code.
table.local-time-zone

Batch Streaming		“default”StringThe 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.plan.compile.catalog-objects

Batch Streaming		ALL

Enum

Strategy how to persist catalog objects such as tables, functions, or data types into a plan during compilation.

It influences the need for catalog metadata to be present during a restore operation and affects the plan size.

This configuration option does not affect anonymous/inline or temporary objects. Anonymous/inline objects will be persisted entirely (including schema and options) if possible or fail the compilation otherwise. Temporary objects will be persisted only by their identifier and the object needs to be present in the session context during a restore.

Possible values:
  • “ALL”: All metadata about catalog tables, functions, or data types will be persisted into the plan during compilation. For catalog tables, this includes the table’s identifier, schema, and options. For catalog functions, this includes the function’s identifier and class. For catalog data types, this includes the identifier and entire type structure. With this strategy, the catalog’s metadata doesn’t have to be available anymore during a restore operation.
  • “SCHEMA”: In addition to an identifier, schema information about catalog tables, functions, or data types will be persisted into the plan during compilation. A schema allows for detecting incompatible changes in the catalog during a plan restore operation. However, all other metadata will still be retrieved from the catalog.
  • “IDENTIFIER”: Only the identifier of catalog tables, functions, or data types will be persisted into the plan during compilation. All metadata will be retrieved from the catalog during a restore operation. With this strategy, plans become less verbose.
table.plan.force-recompile

Streaming		falseBooleanWhen false COMPILE PLAN statement will fail if the output plan file is already existing, unless the clause IF NOT EXISTS is used. When true COMPILE PLAN will overwrite the existing output plan file. We strongly suggest to enable this flag only for debugging purpose.
table.plan.restore.catalog-objects

Batch Streaming		ALL

Enum

Strategy how to restore catalog objects such as tables, functions, or data types using a given plan and performing catalog lookups if necessary. It influences the need for catalog metadata to bepresent and enables partial enrichment of plan information.

Possible values:
  • “ALL”: Reads all metadata about catalog tables, functions, or data types that has been persisted in the plan. The strategy performs a catalog lookup by identifier to fill in missing information or enrich mutable options. If the original object is not available in the catalog anymore, pipelines can still be restored if all information necessary is contained in the plan.
  • “ALL_ENFORCED”: Requires that all metadata about catalog tables, functions, or data types has been persisted in the plan. The strategy will neither perform a catalog lookup by identifier nor enrich mutable options with catalog information. A restore will fail if not all information necessary is contained in the plan.
  • “IDENTIFIER”: Uses only the identifier of catalog tables, functions, or data types and always performs a catalog lookup. A restore will fail if the original object is not available in the catalog anymore. Additional metadata that might be contained in the plan will be ignored.
table.resources.download-dir

Batch Streaming		System.getProperty(“java.io.tmpdir”)StringLocal directory that is used by planner for storing downloaded resources.
table.rtas-ctas.atomicity-enabled

Batch Streaming		falseBooleanSpecifies if the CREATE TABLE/REPLACE TABLE/CREATE OR REPLACE AS SELECT statement is executed atomically. By default, the statement is non-atomic. The target table is created/replaced on the client side, and it will not be rolled back even though the job fails or is canceled. If set this option to true and the underlying DynamicTableSink implements the SupportsStaging interface, the statement is expected to be executed atomically, the behavior of which depends on the actual DynamicTableSink.
table.sql-dialect

Batch Streaming		“default”StringThe 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.

KeyDefaultTypeDescription
sql-client.display.color-schema

Batch Streaming		“DEFAULT”StringSQL highlight color schema to be used at SQL client. Possible values: ‘default’, ‘dark’, ‘light’, ‘chester’, ‘vs2010’, ‘solarized’, ‘obsidian’, ‘geshi’
sql-client.display.max-column-width

Batch Streaming		30IntegerDeprecated, please use table.display.max-column-width instead. When printing the query results, this parameter determines the number of characters shown on screen before truncating. This only applies to columns with variable-length types (e.g. CHAR, VARCHAR, STRING) in streaming mode. Fixed-length types and all types in batch mode are printed using a deterministic column width.
sql-client.display.print-time-cost

Batch		trueBooleanDetermine whether to display the time consumption of the query. By default, no query time cost will be displayed.
sql-client.display.show-line-numbers

Batch Streaming		falseBooleanDetermines whether there should be shown line numbers in multiline SQL or not.
sql-client.execution.max-table-result.rows

Batch Streaming		1000000IntegerThe 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

Determines how the query result should be displayed.

Possible values:
  • “TABLE”: Materializes results in memory and visualizes them in a regular, paginated table representation.
  • “CHANGELOG”: Visualizes the result stream that is produced by a continuous query.
  • “TABLEAU”: Display results in the screen directly in a tableau format.
sql-client.verbose

Batch Streaming		falseBooleanDetermine 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.