Status

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JIRA: here (<- link to https://issues.apache.org/jira/browse/FLINK-XXXX)

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Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).

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We propose to make the following API changes to support dynamic pattern changing in CEP.

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PatternProcessor Interface

The term "rulePatternProcessor" defines a certain pattern, how to match the pattern, and how to process the found matches. A rule pattern processor extends beyond a pattern to also include all the other relevant functions. A brief illustration to their relationship is as below.The starting As shown in the wireframe below, Process 1 and Process 2 are equivalent process.

The starting point of introducing "RulePatternProcessor" is trying to deal with situations like follows:

• While one pattern requires input data to be grouped by user_id , another pattern might require data to be grouped by item_id .

• Users might want to distinguish results that match different patterns and deal with them differently.

Simply having multiple patterns might not be able to deal with situations like above, and the concept Rule  PatternProcessor can handle them properly.

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We propose to add PatternProcessor interface as below. The properties of the rule pattern processor are required by CEP operator in order to function correctly.

/**
 * Base class for a pattern processor definition.
 *
 * <p>A pattern processor defines a {@link Pattern}, how to match the pattern, and how to process
 * the found matches.
 *
 * @param <IN> Base type of the elements appearing in the pattern.
 * @param <OUT> Type of produced elements based on found matches.
 */
@PublicEvolving
public interface PatternProcessor<IN, OUT> extends Serializable, Versioned {

    /**
     * Returns the ID of the pattern processor.
     *
     * @return The ID of the pattern processor.
     */
    String getId();

    /**
     * Returns the scheduled time at which the pattern processor should come into effective.
     *
     * <p>If the scheduled time is earlier than current event/processing time, the pattern processor
     * will immediately become effective.
     *
     * <p>If the pattern processor should always become effective immediately, the scheduled time
     * can be set to {@code Long.MIN_VALUE}: {@value Long#MIN_VALUE}.
     *
     * @return The scheduled time.
     */
    default Long getTimestamp() {
        return Long.MIN_VALUE;
    }

    /**
     * Returns the {@link Pattern} to be matched.
     *
     * @return The pattern of the pattern processor.
     */
    Pattern<IN, ?> getPattern();

    /**
     * Returns the {@link KeySelector} to extract the key of the elements appearing in the pattern.
     *
     * <p>Only data with the same key can be chained to match a pattern. If extracted key is null,
     * the behavior is to reuse current key if is {@link KeyedStream}, or allocate the same key for
     * all input data if is not {@link KeyedStream}.
     *
     * @return The key selector of the pattern processor.
     */
    @Nullable
    KeySelector<IN, ?> getKeySelector();

    /**
     * Get the {@link PatternProcessFunction} to process the found matches for the pattern.
     *
     * @return The pattern process function of the pattern processor.
     */
    PatternProcessFunction<IN, OUT> getPatternProcessFunction();
}

Given the needs of data exchange and state storage, the

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PatternProcessor must be serializable, which requires that the classes involved in the

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PatternProcessor must also be serializable, for example, make Pattern implement Serializable.

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PatternProcessorManager Interface

We propose to introduce the RuleManagerPatternProcessorManager interface. The manager handles the rule pattern processor updates and provides information about current active rulespattern processors.

Its possible implementation includes components in Flink job that need to receive rule pattern processor update notifications, like CepOperator or its OperatorCoordinator. This means that this interface will only have internal implementations. Users only need to use this interface, not to inherit or create new instance of child classes of this interface.

/**
 * This manager handles updated rulespattern processors and manages current rulespattern processors.
 *
 * @param <IN> Base type of the elements appearing in the pattern.
 * @param <OUT> Type of produced elements based on found matches.
 */
@PublicEvolving
public interface RuleManager<INPatternProcessorManager<IN, OUT> {
    /**
     * DealsDeal with the notification that rulespattern processors are updated.
     *
     * @param rulespatternProcessors A list of all latest pattern rulesprocessors.
     */
    void onRulesUpdatedonPatternProcessorsUpdated(List<Rule<INList<PatternProcessor<IN, OUT>> rulespatternProcessors);

    /**
     * Returns the current rulespattern processors managed.
     *
     * @return The current rulespattern processors.
     */
    List<Rule<INList<PatternProcessor<IN, OUT>> getCurrentRulesgetCurrentPatternProcessors();
}

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PatternProcessorDiscoverer Interface

We introduce the RuleDiscovererPatternProcessorDiscoverer interface, which discovers the rule pattern processor changes and notifies the RuleManagerPatternProcessorManager of the collected rule pattern processor updates. This interface also serves to provide initial rulespattern processors.

/**
 * Interface that discovers rulepattern processor changesupdates, notifies {@link RuleManagerPatternProcessorManager} of rule
 * pattern processor updates and provides
 * the initial rulespattern processors.
 *
 * @param <IN> Base type of the elements appearing in the pattern.
 * @param <OUT> Type of produced elements based on found matches.
 */
@PublicEvolving
public interface RuleDiscoverer<INPatternProcessorDiscoverer<IN, OUT> extends Closeable {
    /**
     * Discover the rulepattern processor changesupdates.
     *
     * <p>In dynamic pattern ruleprocessor changingupdate case, this function should be a continuous process to
 check  rule
  * check pattern *processor updates and notify the {@link RuleManagerPatternProcessorManager}.
     */
    void discoverRuleChangesdiscoverPatternProcessorUpdates(RuleManager<INPatternProcessorManager<IN, OUT> manager) throws Exception;

    /**
     * Returns the rulespattern processors an operator should be set up with.
     *
     * @return The initial rulespattern processors.
     */
    List<Rule<INList<PatternProcessor<IN, OUT>> getInitialRulesgetInitialPatternProcessors();
}

Users should be responsible for creating child classes for this interface, as the possible ways to collect rules pattern processors are unlimited. Still, we can provide several abstract class that can handle most use cases, like the following AbstractPeriodicRuleDiscovererAbstractPeriodicPatternProcessorDiscoverer that periodically checks for updates.

/** Class that periodically checks for rulepattern processor updates. */
public abstract class AbstractPeriodicRuleDiscoverer<INAbstractPeriodicPatternProcessorDiscoverer<IN, OUT>
        implements RuleDiscoverer<INPatternProcessorDiscoverer<IN, OUT> {
    private final List<Rule<INList<PatternProcessor<IN, OUT>> initRulesinitPatternProcessors;
    private final long intervalMillis;

    /**
     * Constructor.
     *
     * @param intervalMillis Time interval in milliseconds at which to check updates.
     */
    public AbstractPeriodicRuleDiscoverer(List<Rule<INAbstractPeriodicPatternProcessorDiscoverer(
            List<PatternProcessor<IN, OUT>> initRulesinitPatternProcessors, long intervalMillis) {
        this.initRulesinitPatternProcessors = initRulesinitPatternProcessors;
        this.intervalMillis = intervalMillis;
    }

    /** Checks whether there is rulepattern processor update. */
    public abstract boolean checkRuleUpdatecheckPatternProcessorUpdate();

    /** Returns the latest rulespattern processors. */
    public abstract List<Rule<INList<PatternProcessor<IN, OUT>> getLatestRulesgetLatestPatternProcessors();

    @Override
    public void discoverRuleChanges(RuleManager<INdiscoverPatternProcessorUpdates(
            PatternProcessorManager<IN, OUT> ruleManagerpatternProcessorManager) {
        while (true) {
            if (checkRuleUpdatecheckPatternProcessorUpdate()) {
                List<Rule<INList<PatternProcessor<IN, OUT>> rulespatternProcessors = getLatestRulesgetLatestPatternProcessors();
                ruleManagerpatternProcessorManager.onRulesUpdatedonPatternProcessorsUpdated(rulespatternProcessors);
            }
            try {
                Thread.sleep(intervalMillis);
            } catch (InterruptedException e) {
                e.printStackTrace();
                break;
            }
        }
    }

    @Override
    public List<Rule<INList<PatternProcessor<IN, OUT>> getInitialRulesgetInitialPatternProcessors() {
        return initRulesinitPatternProcessors;
    }
}

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PatternProcessorDiscovererFactory Interface

The RuleDiscovererFactoryPatternProcessorDiscovererFactory is introduced to create the RuleDiscovererPatternProcessorDiscoverer with the RuleManagerPatternProcessorManager, that requires RuleDiscovererPatternProcessorDiscoverer to notify RuleManagerPatternProcessorManager to deal with the rule pattern processor changes.

/**
 * A factory for {@link RuleDiscovererPatternProcessorDiscoverer}. The created {@link RuleDiscovererPatternProcessorDiscoverer}
 * should notify {@link
 * RuleManagerPatternProcessorManager} to deal with the rulepattern processor changesupdates.
 *
 * @param <IN> Base type of the elements appearing in the pattern.
 * @param <OUT> Type of produced elements based on found matches.
 */
public interface RuleDiscovererFactory<INPatternProcessorDiscovererFactory<IN, OUT> extends Serializable {
    /**
     * Creates a {@link RuleDiscovererPatternProcessorDiscoverer}.
     *
     * @return A {@link RuleDiscovererPatternProcessorDiscoverer} instance.
     */
    RuleDiscoverer<INPatternProcessorDiscoverer<IN, OUT> createRuleDiscoverercreatePatternProcessorDiscoverer() throws Exception;
}

Add CEP.

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patternProcess() method

CEP adds the rulepatternProcess() method to apply the dynamic rules pattern processors onto the input DataStream and create new DataStream containing the processed results of matched CEP rulespattern processors.

public class CEP {
    /**
     * Creates a {@link DataStream} containing the processed results of matched CEP rulespattern
     * processors.
     *
     * @param input DataStream containing the input events
     * @param factory RulePattern processor discoverer factory to create the {@link RuleDiscoverer
     *     PatternProcessorDiscoverer}
     * @param <IN> typeType of the elements appearing in the pattern
     * @param <OUT> typeType of produced elements based on found matches
     * @return Resulting data stream
     */
    public static <IN, OUT> DataStream<OUT> rulepatternProcessors(
            DataStream<IN> input, RuleDiscovererFactory<INPatternProcessorDiscovererFactory<IN, OUT> factory) {...}
}

Example Usage

This section provides code snippets that shows how users can use the APIs proposed above to change CEP rules dynamicallypattern processors dynamically.

Suppose the CEP library provided an implementation of RuleDiscovererPatternProcessorDiscoverer, called PeriodDBRuleDiscovererPatternProcessorDiscoverer, that periodically checks a database for rule pattern processor updates and initializes rules pattern processors from a fixed rule pattern processor set according to the json.

/**
 * Implementation of {@link RuleDiscovererPatternProcessorDiscoverer} that periodically checks a database for rule updates and
 * initializes the {@link Rule} from a fixed rule set according to information retrieved from the databasepattern processor updates.
 */
public class PeriodDBRuleDiscoverer<INPeriodDBPatternProcessorDiscoverer<IN, OUT> implements RuleDiscoverer<INPatternProcessorDiscoverer<IN, OUT> {

    @Override
    public void discoverRuleChangesdiscoverPatternProcessorChanges() {...}

    @Override
    public List<Rule<INList<PatternProcessor<IN, OUT>> getInitialRulesgetInitialPatternProcessors() {...}
}

To start with, users need to provide classes that implement RulePatternProcessor interface. Suppose some class called DummyRuleDummyPatternProcessor is created like below.

public class DummyRuleDummyPatternProcessor implements Rule<EventPatternProcessor<Event, Object> {
    
    public DummyRuleDummyPatternProcessor(int a) {...}

    @Override
    public String getId() {...}
    
    @Override
    public int getVersion() {...}

    @Override
    public Pattern<Event, ?> getPattern() {...}

    @Nullable
    @Override
    public KeySelector<Event, ?> getKeySelector() {...}

    @Override
    public PatternProcessFunction<Event, Object> getPatternProcessFunction() {...}
}

In order to use PeriodDBRuleDiscovererPeriodDBPatternProcessorDiscoverer, users need to create an instance of PeriodDBRuleDiscovererPeriodDBPatternProcessorDiscovererFactory.

PeriodDBRuleDiscovererFactory<EventPeriodDBPatternProcessorDiscovererFactory<Event, Object> factory = new PeriodDBRuleDiscovererFactoryPeriodDBPatternProcessorDiscovererFactory();

Then users create DataStream with input data stream and this factory.

DataStream<Event> inputStream = env.fromElements(...);

DataStream<Object> outputStream = CEP.rulepatternProcessors(inputStream, factory);

After Flink job has been configured to support dynamic rule pattern processor and successfully submitted, users can update rules pattern processors by configuring rules pattern processors in their database, recording them in a table as follows.

Information like rule pattern processor update or exception will be recorded in Flink's log system. Users can check the log to see, for example, whether a rule pattern processor update succeeded or not.

The general process of the example described in this section and some general implementation idea is shown in the wireframe below. Operator Coordinator on JobManager will read rule pattern processor updates from users' database and send rule pattern processor updates to subtasks, and as a result the matched & processed results produced by subtasks are influenced.

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Proposed Changes

The general structure that supports dynamic rules in a job graph is shown in the wireframe below. When an input event data comes from upstream operator, it goes through the following process to produce a matched & processed record.

1. Key Generating Operator computes the key of the input data according to the KeySelector provided by Rules, and attach the key along with the input data. If there are N rules, then there will be N records with the same input data and different keys sent downstream.
2. Input data is sent to different downstream operator subtasks by calling keyBy() operation on them. keyBy() uses the key generated in the previous step.
3. The next operator matches the input data to Patterns provided by Rules, and when a matched sequence is generated, the operator calls the corresponding PatternProcessFunction on that sequence and produce the result to downstream.

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In the wireframe above, Key Generating Operator and Pattern Matching & Processing Operator uses information from the rules and thus need a RuleDiscoverer. Our design of collecting rules and update them inside operators is shown in the wireframe below. In each of the two operators mentioned, there will be rule managers inside each of their Operator Coordinators. The manager in the OperatorCoordinator serves to discover rule changes and convert the information into rules. Then the subtasks, after receiving the latest rules from Operator Coordinator, will make the update.

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Supporting multiple & dynamic rules inside CEP operators

Currently CepOperator uses NFAState and NFA to deal with input data. As it has not supported multiple patterns yet, all input data are dealt with by the same NFAState.

In order to support multiple patterns in one operator, the one NFA needs to be replaced by a list or map of NFA, each corresponds to a Rule. When input data triggers the processElement() method, the data will be matched against all rules in the operator. Adding or deleting rules means adding or deleting entries in the list or map.

Scheduling rules

In order to make the behavior of rule updates more deterministic, users can schedule the time at which an updated rule should come into effect, which is reflected by the getTimestamp() method in Rule .

When a new set of rules is provided by RuleDiscoverer , they will first be cached in operators. When a watermark arrived at the operator, it will check the timestamp of the watermark against the scheduled time of cached rules and update rules accordingly.

Failover

During checkpoints, the operators mentioned above will store serialized rules and partially matched results inside state backend and restore them during recovery. In this way rules or matches will not be lost during failover.

Common case behavior analysis

When a rule is deleted or replaced, partially matched results related to the rule is directly deprecated and will no longer match to incoming input data.

If there is no rule in a CepOperator, all input data will be deprecated as they cannot match to any of the existing patterns.

If IDs of the List<Rule>  provided by RuleDiscoverer  duplicate with existing rules, versions of the Rule s will be checked. existing rules will be preserved if the versions of the two are the same, or it will be replaced by the newer one otherwise.

Given that the proposed design offers eventual consistency, there could be a period of time just after a rule update happens when inconsistency exists among subtasks of CepOperators, which means they do not work on the same set of rules. This inconsistency is caused by the fact that OperatorCoordinator cannot send OperatorEvent to subtasks simutaneously, and this period of time usually lasts for milliseconds. As can be illustrated in the graph below, fake alerts ("AC" instead of "ABC") might be generated during that period.

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Compatibility, Deprecation, and Migration Plan

The changes proposed in this FLIP is backward compatible with existing APIs of Flink CEP. We propose to change the APIs directly without deprecation period.

Current CEP.pattern() methods still exist after changes of this FLIP is introduced, and static CEP patterns created by these methods can still preserve their function, performance and job structure.

Test Plan

We will use unit tests to cover the correctness of introduced features.

Future Work

Service Degradation

As all CEP patterns can be placed in the same operator of the same Flink job in the features we proposed, the error of a single pattern could cause the failure of the whole Flink job, thus affecting the availability of other CEP patterns.

In order to solve this problem, we plan to make CepOperators able to handle exceptions within a rule's scope. When the matching or processing function of a certain rule throws exceptions, the operator should catch this exception, convert it into error messages and sent to side output to call for external solution. The rule that threw the exception will be temporarily disabled on that subtask while other rules work unaffected.

We plan to expose this feature as a configurable property. The default behavior is still causing the whole Flink job to fail.

Table/SQL API Support

Currently we only propose the design for multiple/dynamic rules in DataStream API, but our design can also be extended into Table/SQL API.

In order to achieve the support, we need to add a keyword in SQL like follows, similar to the existing MATCH_RECOGNIZE keyword.

SELECT * FROM input_table RULE(
	`rule_discoverer_factory` = custom_rule_discoverer_factory
)

While MATCH_RECOGNIZE is standard SQL released by the International Organization for Standardization (ISO), we have not found any standard expression format that supports multiple & dynamic match in SQL. Thus the example above is still a naive idea from us and might need further refinement.

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Add Key Generating and Pattern Matching & Processing Operator

In order to achive the functions described in pubic API, we propose to add two stream operators in the stream graph.

The first is a key generating operator. For each input data and each active PatternProcessor, the operator would generate a key according to the KeySelector in the PatternProcessor and attach the key to that data.

After that, the stream graph would do a keyBy() operation on the input data, using the generated key. In this way the downstream operator could have data with the same key aggregated on the same subtask, and that key can be dyncamically updated.

Then the second operator serves to match data to patterns and to process the matched results with the PatternProcessFunctions. The first duty is almost the same as that of CepOperator, except that it needs to handle multiple and dynamic patterns now. The second duty has been achieved by PatternStream.process() method, but now it needs to be achieved by inner implementations.

The general structure that supports dynamic pattern processors in a job graph is shown in the wireframe below.

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Add OperatorCoordinator for the two operators

We propose to add OperatorCoordinators for the two operators, which is responsible for listening for updates and keeping the consistency among subtasks. The OperatorCoordinator has been proposed in FLIP-27 and most mechanisms we need would have been achieved in its implementation, so we should only need to inherit OperatorCoordinator and achieve the unique implementation details of these two operators.

In the wireframe above, Key Generating Operator and Pattern Matching & Processing Operator uses information from the pattern processors and thus need a PatternProcessorDiscoverer. Our design of collecting pattern processors and update them inside operators is shown in the wireframe below. In each of the two operators mentioned, there will be pattern processor discoverers inside each of their Operator Coordinators. The discoverer in the OperatorCoordinator serves to discover pattern processor changes and convert the information into pattern processors. Then the subtasks, after receiving the latest pattern processors from Operator Coordinator, will make the update.

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Possible implementations of pattern processor discovery and serialization format

The public interfaces we propose above aims to yield the flexibility of discovering pattern processors and serializing them to users. It is also possible that we provide some built-in implementations for common uses. Some possible ways for the discoverer to discover pattern processors are as follows:

• The discoverer periodically checks a file in filesystem or a table in database for updates

• The discoverer listens on external commands, like HTTP requests, for updates

And the format to transmit serialized pattern processors to Flink jobs before it is converted to Java objects can be follows:

• Directly serializing Java objects using Java serialization or Kryo

• Using readable formats like JSON or XML

• Using the standard MATCH_RECOGNIZE grammar in SQL proposed by ISO

Supporting multiple & dynamic pattern processors inside CEP operators

Currently CepOperator uses NFAState and NFA to deal with input data. As it has not supported multiple patterns yet, all input data are dealt with by the same NFAState.

In order to support multiple patterns in one operator, the one NFA needs to be replaced by a list or map of NFA, each corresponds to a PatternProcessor. When input data triggers the processElement() method, the data will be matched against all pattern processors in the operator. Adding or deleting pattern processors means adding or deleting entries in the list or map.

Scheduling pattern processors

In order to make the behavior of pattern processor updates more deterministic, users can schedule the time at which an updated pattern processor should come into effect, which is reflected by the getTimestamp() method in PatternProcessor .

When a new set of pattern processors is provided by PatternProcessorDiscoverer , they will first be cached in operators. When a watermark arrived at the operator, it will check the timestamp of the watermark against the scheduled time of cached pattern processors and update pattern processors accordingly.

Failover

During checkpoints, the operators mentioned above will store serialized pattern processors and partially matched results inside state backend and restore them during recovery. In this way pattern processors or matches will not be lost during failover.

Common case behavior analysis

When a pattern processor is deleted or replaced, partially matched results related to the pattern processor is directly deprecated and will no longer match to incoming input data.

If there is no pattern processor in a CepOperator, all input data will be deprecated as they cannot match to any of the existing patterns.

If IDs of the List<PatternProcessor>  provided by PatternProcessorDiscoverer  duplicate with existing pattern processors, versions of the PatternProcessor s will be checked. existing pattern processors will be preserved if the versions of the two are the same, or it will be replaced by the newer one otherwise.

Given that the proposed design offers eventual consistency, there could be a period of time just after a pattern processor update happens when inconsistency exists among subtasks of CepOperators, which means they do not work on the same set of pattern processors. This inconsistency is caused by the fact that OperatorCoordinator cannot send OperatorEvent to subtasks simultaneously, and this period of time usually lasts for milliseconds. As can be illustrated in the graph below, fake alerts ("AC" instead of "ABC") might be generated during that period.

Image Added

Compatibility, Deprecation, and Migration Plan

The changes proposed in this FLIP is backward compatible with existing APIs of Flink CEP. We propose to change the APIs directly without deprecation period.

Current CEP.pattern() methods still exist after changes of this FLIP is introduced, and static CEP patterns created by these methods can still preserve their function, performance and job structure.

Test Plan

We will use unit tests to cover the correctness of introduced features.

Future Work

Service Degradation

As all CEP patterns can be placed in the same operator of the same Flink job in the features we proposed, the error of a single pattern could cause the failure of the whole Flink job, thus affecting the availability of other CEP patterns.

In order to solve this problem, we plan to make CepOperators able to handle exceptions within a PatternProcessor's scope. When the matching or processing function of a certain pattern processor throws exceptions, the operator should catch this exception, convert it into error messages and sent to side output to call for external solution. The pattern processor that threw the exception will be temporarily disabled on that subtask while other pattern processors work unaffected.

We plan to expose this feature as a configurable property. The default behavior is still causing the whole Flink job to fail.

Table/SQL API Support

Currently we only propose the design for multiple/dynamic pattern processors in DataStream API, but our design can also be extended into Table/SQL API.

In order to achieve the support, we need to add a keyword in SQL like follows, similar to the existing MATCH_RECOGNIZE keyword.

SELECT T.aid, T.bid, T.cid
FROM MyTable
    MATCH_RECOGNIZE_DYNAMIC (
      pattern_processor_discoverer_factory = custom_pattern_processor_discoverer_factory
    ) AS T

While MATCH_RECOGNIZE is standard SQL released by the International Organization for Standardization (ISO), we have not found any standard expression format that supports multiple & dynamic match in SQL. Thus the example above is just a simple example showing how the API might look like. Further refinement of the API is required when it comes to detailed design.

As for implementation, Flink's infrastructure for Table/SQL can recognize the keyword above and create corresponding StreamOperators. The operators can accept the PatternProcessorDiscovererFactory and pass it to the existing implementations proposed in this FLIP, which means Table/SQL and DataStream implementation of this functionality can share a lot in common.

Rejected Alternatives

• Have each subtask of an operator make the update on their own

  • It is hard to achieve consistency.

    • Though the time interval that each subtask makes the update can be the same, the absolute time they make the update might be different. For example, one makes updates at 10:00, 10:05, etc, while another does it at 10:01, 10:06. In this case the subtasks might never processing data with the same set of pattern processors.

    • In cases when users want to control the updates with external commands, it might require extra work for them to make sure that the command has reached all the subtasks.

• Using a DataStream<PatternProcessor<IN, OUT>>, instead of OperatorCoordinators, to pass updates

  • Unlike input data that is flowing in as a stream, updates are actually control messages that, instead of passing in as datastream, should be sent out by a control center like JobManager. OperatorCoordinators meet this semantics and thus is a more proper choice.