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*
* http://www.apache.org/licenses/LICENSE-2.0
*
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* limitations under the License.
*/
package org.apache.flink.table.api.config;
import org.apache.flink.annotation.PublicEvolving;
import org.apache.flink.annotation.docs.Documentation;
import org.apache.flink.configuration.ConfigOption;
import org.apache.flink.configuration.MemorySize;
import org.apache.flink.configuration.description.Description;
import static org.apache.flink.configuration.ConfigOptions.key;
import static org.apache.flink.configuration.description.TextElement.code;
/**
* This class holds configuration constants used by Flink's table planner module.
*
* NOTE: All option keys in this class must start with "table.optimizer".
*/
@PublicEvolving
public class OptimizerConfigOptions {
// ------------------------------------------------------------------------
// Optimizer Options
// ------------------------------------------------------------------------
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_AGG_PHASE_STRATEGY =
key("table.optimizer.agg-phase-strategy")
.enumType(AggregatePhaseStrategy.class)
.defaultValue(AggregatePhaseStrategy.AUTO)
.withDescription(
"Strategy for aggregate phase. Only AUTO, TWO_PHASE or ONE_PHASE can be set.\n"
+ "AUTO: No special enforcer for aggregate stage. Whether to choose two stage aggregate or one"
+ " stage aggregate depends on cost. \n"
+ "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.\n"
+ "ONE_PHASE: Enforce to use one stage aggregate which only has CompleteGlobalAggregate.");
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH)
public static final ConfigOption TABLE_OPTIMIZER_BROADCAST_JOIN_THRESHOLD =
key("table.optimizer.join.broadcast-threshold")
.longType()
.defaultValue(1024 * 1024L)
.withDescription(
"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.");
@Documentation.TableOption(execMode = Documentation.ExecMode.STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_DISTINCT_AGG_SPLIT_ENABLED =
key("table.optimizer.distinct-agg.split.enabled")
.booleanType()
.defaultValue(false)
.withDescription(
"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.");
@Documentation.TableOption(execMode = Documentation.ExecMode.STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_DISTINCT_AGG_SPLIT_BUCKET_NUM =
key("table.optimizer.distinct-agg.split.bucket-num")
.intType()
.defaultValue(1024)
.withDescription(
"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.");
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_REUSE_SUB_PLAN_ENABLED =
key("table.optimizer.reuse-sub-plan-enabled")
.booleanType()
.defaultValue(true)
.withDescription(
"When it is true, the optimizer will try to find out duplicated sub-plans and reuse them.");
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_REUSE_SOURCE_ENABLED =
key("table.optimizer.reuse-source-enabled")
.booleanType()
.defaultValue(true)
.withDescription(
"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.key()
+ " is true.");
/** This configuration will be removed in Flink 2.0. */
@Deprecated
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH)
public static final ConfigOption TABLE_OPTIMIZER_SOURCE_AGGREGATE_PUSHDOWN_ENABLED =
key("table.optimizer.source.aggregate-pushdown-enabled")
.booleanType()
.defaultValue(true)
.withDescription(
"When it is true, the optimizer will push down the local aggregates into "
+ "the TableSource which implements SupportsAggregatePushDown.");
/** This configuration will be removed in Flink 2.0. */
@Deprecated
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_SOURCE_PREDICATE_PUSHDOWN_ENABLED =
key("table.optimizer.source.predicate-pushdown-enabled")
.booleanType()
.defaultValue(true)
.withDescription(
"When it is true, the optimizer will push down predicates into the FilterableTableSource. "
+ "Default value is true.");
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_SOURCE_REPORT_STATISTICS_ENABLED =
key("table.optimizer.source.report-statistics-enabled")
.booleanType()
.defaultValue(true)
.withDescription(
"When 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.");
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_JOIN_REORDER_ENABLED =
key("table.optimizer.join-reorder-enabled")
.booleanType()
.defaultValue(false)
.withDescription("Enables join reorder in optimizer. Default is disabled.");
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_BUSHY_JOIN_REORDER_THRESHOLD =
key("table.optimizer.bushy-join-reorder-threshold")
.intType()
.defaultValue(12)
.withDescription(
"The 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.");
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH)
public static final ConfigOption TABLE_OPTIMIZER_MULTIPLE_INPUT_ENABLED =
key("table.optimizer.multiple-input-enabled")
.booleanType()
.defaultValue(true)
.withDescription(
"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.");
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_DYNAMIC_FILTERING_ENABLED =
key("table.optimizer.dynamic-filtering.enabled")
.booleanType()
.defaultValue(true)
.withDescription(
"When 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.");
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH)
public static final ConfigOption TABLE_OPTIMIZER_RUNTIME_FILTER_ENABLED =
key("table.optimizer.runtime-filter.enabled")
.booleanType()
.defaultValue(false)
.withDescription(
"A flag to enable or disable the runtime filter. "
+ "When it is true, the optimizer will try to inject a runtime filter for eligible join.");
/**
* The data volume of build side needs to be under this value. If the data volume of build side
* is too large, the building overhead will be too large, which may lead to a negative impact on
* job performance.
*/
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH)
public static final ConfigOption
TABLE_OPTIMIZER_RUNTIME_FILTER_MAX_BUILD_DATA_SIZE =
key("table.optimizer.runtime-filter.max-build-data-size")
.memoryType()
.defaultValue(MemorySize.parse("150m"))
.withDescription(
"Max data volume threshold of the runtime filter build side. "
+ "Estimated data volume needs to be under this value to try to inject runtime filter.");
/**
* The data volume of probe side needs to be over this value. If the data volume on the probe
* side is too small, the overhead of building runtime filter is not worth it.
*/
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH)
public static final ConfigOption
TABLE_OPTIMIZER_RUNTIME_FILTER_MIN_PROBE_DATA_SIZE =
key("table.optimizer.runtime-filter.min-probe-data-size")
.memoryType()
.defaultValue(MemorySize.parse("10g"))
.withDescription(
Description.builder()
.text(
"Min 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 %s.",
code(
TABLE_OPTIMIZER_RUNTIME_FILTER_MAX_BUILD_DATA_SIZE
.key()))
.build());
/**
* The filtering ratio of runtime filter needs to be over this value. A low filter rate is not
* worth it to build runtime filter.
*/
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH)
public static final ConfigOption TABLE_OPTIMIZER_RUNTIME_FILTER_MIN_FILTER_RATIO =
key("table.optimizer.runtime-filter.min-filter-ratio")
.doubleType()
.defaultValue(0.5)
.withDescription(
"Min filter ratio threshold of the runtime filter. "
+ "Estimated filter ratio needs to be over this value to try to inject runtime filter.");
@Documentation.TableOption(execMode = Documentation.ExecMode.STREAMING)
public static final ConfigOption
TABLE_OPTIMIZER_NONDETERMINISTIC_UPDATE_STRATEGY =
key("table.optimizer.non-deterministic-update.strategy")
.enumType(NonDeterministicUpdateStrategy.class)
.defaultValue(NonDeterministicUpdateStrategy.IGNORE)
.withDescription(
Description.builder()
.text(
"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:")
.linebreak()
.text(
"1. Non-deterministic functions, include scalar, table, aggregate functions, both builtin and custom ones.")
.linebreak()
.text("2. LookupJoin on an evolving source")
.linebreak()
.text(
"3. Cdc-source carries metadata fields which are system columns, not belongs to the entity data itself.")
.linebreak()
.linebreak()
.text(
"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.")
.linebreak()
.text(
"Default value is `IGNORE`, the optimizer does no changes.")
.build());
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_SQL2REL_PROJECT_MERGE_ENABLED =
key("table.optimizer.sql2rel.project-merge.enabled")
.booleanType()
.defaultValue(false)
.withDeprecatedKeys("table.optimizer.sql-to-rel.project.merge.enabled")
.withDescription(
Description.builder()
.text(
"If set to true, it will merge projects when converting SqlNode to RelNode.")
.linebreak()
.text(
"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).")
.build());
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_UNIONALL_AS_BREAKPOINT_ENABLED =
key("table.optimizer.union-all-as-breakpoint-enabled")
.booleanType()
.defaultValue(true)
.withDescription(
"When true, the optimizer will breakup the graph at union-all node "
+ "when it's a breakpoint. When false, the optimizer will skip the union-all node "
+ "even it's a breakpoint, and will try find the breakpoint in its inputs.");
@Documentation.TableOption(execMode = Documentation.ExecMode.BATCH_STREAMING)
public static final ConfigOption
TABLE_OPTIMIZER_REUSE_OPTIMIZE_BLOCK_WITH_DIGEST_ENABLED =
key("table.optimizer.reuse-optimize-block-with-digest-enabled")
.booleanType()
.defaultValue(false)
.withDescription(
"When true, the optimizer will try to find out duplicated sub-plans by "
+ "digest to build optimize blocks (a.k.a. common sub-graphs). "
+ "Each optimize block will be optimized independently.");
@Documentation.TableOption(execMode = Documentation.ExecMode.STREAMING)
public static final ConfigOption TABLE_OPTIMIZER_INCREMENTAL_AGG_ENABLED =
key("table.optimizer.incremental-agg-enabled")
.booleanType()
.defaultValue(true)
.withDescription(
"When both local aggregation and distinct aggregation splitting "
+ "are enabled, a distinct aggregation will be optimized into four aggregations, "
+ "i.e., local-agg1, global-agg1, local-agg2, and global-agg2. We can combine global-agg1 "
+ "and local-agg2 into a single operator (we call it incremental agg because "
+ "it receives incremental accumulators and outputs incremental results). "
+ "In this way, we can reduce some state overhead and resources. Default is enabled.");
/** Strategy for handling non-deterministic updates. */
@PublicEvolving
public enum NonDeterministicUpdateStrategy {
/**
* Try to resolve by planner automatically if exists non-deterministic updates, will raise
* an error when cannot resolve.
*/
TRY_RESOLVE,
/**
* Do nothing if exists non-deterministic updates, the risk of wrong result still exists.
*/
IGNORE
}
}