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).

Motivation

In situations like Risk Controlling or Pattern Matching, it is common that users want to change the pattern that events need to match to while the pattern is still functioning to provide service. In current Flink CEP, a CEP operator has a fixed CEP pattern and does not support changing it. Thus in order to achieve the goal described above, users have to restart flink jobs and wait relatively long update time.

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In this FLIP, we propose to add a couple of APIs and classes to Flink CEP in order to support having multiple patterns in one operator and updating patterns dynamically without stoppling stopping Flink jobs.

Public Interfaces

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

Add

...

PatternProcessor Interface

The term "rulePatternProcessor" defines a certain pattern, how to match the pattern, and how to process the found matches. The properties of the rule are required by CEP operator in order to function correctly.. A pattern processor extends beyond a pattern to also include all the other relevant functions. As shown in the wireframe below, Process 1 and Process 2 are equivalent process.

The starting point of introducing "PatternProcessor" 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 PatternProcessor can handle them properly.

Image Added

We propose to add PatternProcessor interface as below. The properties of the 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 {

/**
 * Base class for a rule definition.
 *
 * <p>A rule 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 Rule<IN, OUT> extends Serializable, Versioned {
    /**
     * Returns the ID of the rule.
     *
     * @return The ID of the rule.
     */
    String getId();

    /**
     * Returns the {@linkID Pattern}of tothe bepattern matchedprocessor.
     *
     * @return The patternID of the rulepattern processor.
     */
    Pattern<IN, ?> getPatternString getId();

    /**
     * Returns the {@linkscheduled KeySelector}time toat extractwhich the keypattern ofprocessor theshould elementscome appearing in the patterninto effective.
     *
     * <p>Only data with the same key can be chained to match a pattern. If extracted key is null, <p>If the scheduled time is earlier than current event/processing time, the pattern processor
     * will immediately become effective.
     *
 the behavior is to reuse* current<p>If keythe ifpattern isprocessor {@link KeyedStream}, or allocateshould always become effective immediately, the samescheduled key fortime
     * allcan inputbe dataset ifto is not{@code Long.MIN_VALUE}: {@link@value KeyedStreamLong#MIN_VALUE}.
     *
     * @return The key selector of the rulescheduled time.
     */
    default @Nullable
Long getTimestamp() {
  KeySelector<IN, ?> getKeySelector();

    return Long.MIN_VALUE;
    }

    /**
     * GetReturns the {@link PatternProcessFunctionPattern} to process the found matches for the patternbe matched.
     *
     * @return The pattern process function of the rulepattern processor.
     */
    PatternProcessFunction<INPattern<IN, OUT>?> getPatternProcessFunctiongetPattern();
}

Given the needs of data exchange and state storage, the Rule must be serializable, which requires that the classes involved in the Rule must also be serializable, for example, make Pattern implement Serializable.

Add RuleManager Interface

We propose to introduce the RuleManager interface. The manager handles the rule updates and provides information about current active rules.

Its possible implementation includes components in Flink job that need to receive rule 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 rules and manages current rules.
 *
 * @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<IN, OUT> {

    /**
     * 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();

    /**
     * Deal with Get the {@link PatternProcessFunction} to process the notificationfound thatmatches rulesfor arethe updatedpattern.
     *
     * @param@return The rulespattern Aprocess listfunction of allthe latestpattern rulesprocessor.
     */
    void onRulesUpdated(List<Rule<INPatternProcessFunction<IN, OUT>>OUT> rules);

    /**
     * Returns the current rules managed.
     *
     * @return The current rules.
     */
    List<Rule<IN, OUT>> getCurrentRules();
}

Add RuleDiscoverer Interface

getPatternProcessFunction();
}

Given the needs of data exchange and state storage, the PatternProcessor must be serializable, which requires that the classes involved in the PatternProcessor must also be serializable, for example, make Pattern implement Serializable.

Add PatternProcessorManager Interface

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

Its possible implementation includes components in Flink job that need to receive 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 interfaceWe introduce the RuleDiscoverer interface, which discovers the rule changes and notifies the RuleManager of the collected rule updates. This interface also serves to provide initial rules.

/**
 * InterfaceThis thatmanager discovershandles ruleupdated changes, notifies {@link RuleManager} of rule updates and provides
 * the initial rulespattern processors and manages current pattern 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<INPatternProcessorManager<IN, OUT> {
    /**
     * Deal Discoverwith the rule changes notification that pattern processors are updated.
     *
     * <p>In@param dynamicpatternProcessors ruleA changinglist case,of thisall functionlatest shouldpattern be a continuous process to check rule
     * updates and notify rule manager.
processors.
      */
    void discoverRuleChanges(onPatternProcessorsUpdated(List<PatternProcessor<IN, OUT>> patternProcessors);

    /**
     * Returns the rulescurrent anpattern operator should be set up withprocessors managed.
     *
     * @return The initialcurrent pattern rulesprocessors.
     */
    List<Rule<INList<PatternProcessor<IN, OUT>> getInitialRulesgetCurrentPatternProcessors();
}

Add PatternProcessorDiscoverer Interface

We introduce the PatternProcessorDiscoverer interface, which discovers the pattern processor changes and notifies the PatternProcessorManager of the collected pattern processor updates. This interface also serves to provide initial pattern processorsUsers should be responsible for creating child classes for this interface, as the possible ways to collect rules are unlimited. Still, we can provide several abstract class that can handle most use cases, like the following AbstractPeriodicRuleDiscoverer that periodically checks for updates.

/**
 * ClassInterface that periodicallydiscovers checks for rule updates.
 */
public abstract class AbstractPeriodicRuleDiscoverer<IN, OUT> implements RuleDiscoverer<IN, OUT> {
    private final List<Rule<IN, OUT>> initRules;
    private final long intervalMillis;
    private final RuleManager<IN, OUT> ruleManager;

    /**
     * Constructor.
     * pattern processor updates, notifies {@link PatternProcessorManager} of
 * pattern processor updates and provides the initial pattern 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 PatternProcessorDiscoverer<IN, OUT> {
    /**
     * @paramDiscover intervalMillisthe Time interval in milliseconds at which to check pattern processor updates.
     */
    public AbstractPeriodicRuleDiscoverer(
* <p>In dynamic pattern processor update case, this function should be a List<Rule<IN,continuous OUT>>process initRules,to
 long intervalMillis, RuleManager<IN, OUT> ruleManager)* {
check pattern processor updates and notify the {@link this.initRules = initRules;PatternProcessorManager}.
     */
    void discoverPatternProcessorUpdates(PatternProcessorManager<IN, OUT> manager) this.intervalMillis = intervalMillis;throws Exception;

    /**
    this.ruleManager =* ruleManager;
Returns the pattern processors }

an operator should be /**set Checksup whetherwith.
 there is rule update. */
    public abstract* boolean checkRuleUpdate();

    /** Returns the latest rules.@return The initial pattern processors.
     */
    public abstract List<Rule<INList<PatternProcessor<IN, OUT>> getLatestRulesgetInitialPatternProcessors();
}

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

/** Class that periodically checks for pattern processor updates. */
public abstract class AbstractPeriodicPatternProcessorDiscoverer<IN, OUT>
        implements PatternProcessorDiscoverer<IN, OUT> {
    private final List<PatternProcessor<IN, OUT>> initPatternProcessors;
    private final long intervalMillis;

    /**
     * Constructor.
     *
    @Override
    public void discoverRuleChanges() {
        while (true) {
            if (checkRuleUpdate()) {
                List<Rule<IN, OUT>> rules = getLatestRules();
     * @param intervalMillis Time interval in milliseconds at which to check ruleManagerupdates.onRulesUpdated(rules);
     */
    public AbstractPeriodicPatternProcessorDiscoverer(
    }
        List<PatternProcessor<IN, OUT>> initPatternProcessors, long tryintervalMillis) {
        this.initPatternProcessors = initPatternProcessors;
        Thread.sleep(intervalMillis)this.intervalMillis = intervalMillis;
    }

    /** Checks whether there }is catchpattern (InterruptedException e) {processor update. */
    public abstract           e.printStackTraceboolean checkPatternProcessorUpdate();

    /** Returns the latest pattern processors. */
    public abstract List<PatternProcessor<IN, OUT>> breakgetLatestPatternProcessors();

    @Override
    public void discoverPatternProcessorUpdates(
   }
        }
 PatternProcessorManager<IN, OUT> patternProcessorManager) }{

    @Override
    public List<Rule<IN, OUT>> getInitialRules(while (true) {
        return initRules;
    }
}

Add RuleDiscovererFactory Interface

The RuleDiscovererFactory is introduced to create the RuleDiscoverer with the RuleManager, that requires RuleDiscoverer to notify RuleManager to deal with the rule changes.

/**
 * A factory for {@link RuleDiscoverer}. The created {@link RuleDiscoverer} should notify {@link
 * RuleManager} to deal with the rule changes.
 *
 * @param <IN> Base type of the elements appearing in the pattern.
 * @param <OUT> Type of produced elements based on found matches.
 */
public interface RuleDiscovererFactory<IN, OUT> extends Serializable {
    /**if (checkPatternProcessorUpdate()) {
                List<PatternProcessor<IN, OUT>> patternProcessors = getLatestPatternProcessors();
                patternProcessorManager.onPatternProcessorsUpdated(patternProcessors);
            }
            try {
     * Creates a {@link RuleDiscoverer}.          Thread.sleep(intervalMillis);
     *
     * @param ruleManager} Thecatch rule(InterruptedException managere) notified.{
       *     @return A {@link RuleDiscoverer} instancee.printStackTrace();
      */
      RuleDiscoverer<IN, OUT> createRuleDiscoverer(RuleManager<IN, OUT> ruleManager);
}

Add CEP.rule() method

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

public class CEP {
break;
            /**}
     * Creates a {@link DataStream}
 containing the processed results}

 of matched CEP rules.@Override
    public *
List<PatternProcessor<IN, OUT>> getInitialPatternProcessors() {
  * @param input DataStream containing the inputreturn eventsinitPatternProcessors;
    }
}

Add PatternProcessorDiscovererFactory Interface

The PatternProcessorDiscovererFactory is introduced to create the PatternProcessorDiscoverer with the PatternProcessorManager, that requires PatternProcessorDiscoverer to notify PatternProcessorManager to deal with the pattern processor changes.

/**
 * @paramA factory Rule discoverer factory to create thefor {@link PatternProcessorDiscoverer}. The created {@link PatternProcessorDiscoverer}
 * should notify {@link RuleDiscoverer}
    PatternProcessorManager} to deal with the pattern processor updates.
 *
 * @param <IN> Base type of the elements appearing in the pattern.
     * @param <OUT> typeType of produced elements based on found matches.
 */
public interface PatternProcessorDiscovererFactory<IN, OUT> *extends @return Resulting data streamSerializable {
     /*/*
     * Creates a  public static <IN, OUT> DataStream<OUT> rule(
     {@link PatternProcessorDiscoverer}.
     *
     * @return A {@link PatternProcessorDiscoverer} instance.
     */
  DataStream<IN> input, RuleDiscovererFactory<INPatternProcessorDiscoverer<IN, OUT> factorycreatePatternProcessorDiscoverer() {...}throws Exception;
}

Example Usage

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

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

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

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

    @Override
    public List<Rule<IN, OUT>> getInitialRules() {...}
}

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

Add CEP.patternProcess() method

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

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

Example Usage

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

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

/**
 * Implementation of {@link PatternProcessorDiscoverer} that periodically checks a database for pattern processor updates.
 */
public class PeriodDBPatternProcessorDiscoverer<IN, OUT> implements PatternProcessorDiscoverer<IN, OUT> {

    @Override
    public void discoverPatternProcessorChanges

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

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

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

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

    @Override
    public PatternProcessFunction<EventList<PatternProcessor<IN, Object>OUT>> getPatternProcessFunctiongetInitialPatternProcessors() {...}
}

It is recommended that users override the implementations' toString(), equals(), and hashCode() methods. Overriding toString() provides better readability when monitoring the rules through logs, and overriding equals() and hashCode() can help to avoid reloading existing rules during a rule update.

In order to use PeriodDBRuleDiscoverer, users need to create an instance of PeriodDBRuleDiscovererFactory.

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

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

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

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

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

...

uid

...

className

...

constructorParams

...

1

...

"org.apache.flink.cep.rule.DummyRule"

...

[{"type": "int", "value": 0}]

...

2

...

"org.apache.flink.cep.rule.DummyRule"

...

[{"type": "int", "value": 1}]

Information like rule update or exception will be recorded in Flink's log system. Users can check the log to see, for example, whether a rule 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 updates from users' database and send rule updates to subtasks, and as a result the matched & processed results produced by subtasks are influenced.

Image Removed

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.

Image Removed

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.

Image Removed

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.

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.

Image Removed

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. A general idea is that UDFs can be defined for CEP.rule() and RuleDiscovererFactory implementations, and users will be able to use them in SQL like this:

...

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

public class DummyPatternProcessor implements PatternProcessor<Event, Object> {
    
    public DummyPatternProcessor(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 PeriodDBPatternProcessorDiscoverer, users need to create an instance of PeriodDBPatternProcessorDiscovererFactory.

PeriodDBPatternProcessorDiscovererFactory<Event, Object> factory = new PeriodDBPatternProcessorDiscovererFactory();

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

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

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

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

Information like pattern processor update or exception will be recorded in Flink's log system. Users can check the log to see, for example, whether a 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 pattern processor updates from users' database and send pattern processor updates to subtasks, and as a result the matched & processed results produced by subtasks are influenced.

Image Added

Proposed Changes

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.

Image Added

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.

Image Added

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.