Implement Apache Flink real-time information enrichment patterns

Stream information processing lets you act on information in actual time. Actual-time information analytics can assist you could have on-time and optimized responses whereas bettering the general buyer expertise.

Knowledge streaming workloads usually require information within the stream to be enriched through exterior sources (equivalent to databases or different information streams). Pre-loading of reference information offers low latency and excessive throughput. Nonetheless, this sample will not be appropriate for sure sorts of workloads:

• Reference information updates with excessive frequency
• The streaming utility must make an exterior name to compute the enterprise logic
• Accuracy of the output is vital and the applying shouldn’t use stale information
• Cardinality of reference information may be very excessive, and the reference dataset is just too huge to be held within the state of the streaming utility

For instance, for those who’re receiving temperature information from a sensor community and must get further metadata of the sensors to investigate how these sensors map to bodily geographic places, it’s worthwhile to enrich it with sensor metadata information.

Apache Flink is a distributed computation framework that enables for stateful real-time information processing. It offers a single set of APIs for constructing batch and streaming jobs, making it straightforward for builders to work with bounded and unbounded information. Amazon Managed Service for Apache Flink (successor to Amazon Kinesis Knowledge Analytics) is an AWS service that gives a serverless, totally managed infrastructure for operating Apache Flink functions. Builders can construct extremely obtainable, fault tolerant, and scalable Apache Flink functions with ease and without having to develop into an skilled in constructing, configuring, and sustaining Apache Flink clusters on AWS.

You need to use a number of approaches to counterpoint your real-time information in Amazon Managed Service for Apache Flink relying in your use case and Apache Flink abstraction stage. Every methodology has totally different results on the throughput, community visitors, and CPU (or reminiscence) utilization. For a common overview of information enrichment patterns, discuss with Widespread streaming information enrichment patterns in Amazon Managed Service for Apache Flink.

This submit covers how one can implement information enrichment for real-time streaming occasions with Apache Flink and how one can optimize efficiency. To check the efficiency of the enrichment patterns, we ran efficiency testing based mostly on artificial information. The results of this check is beneficial as a common reference. It’s vital to notice that the precise efficiency on your Flink workload will rely upon varied and various factors, equivalent to API latency, throughput, dimension of the occasion, and cache hit ratio.

We talk about three enrichment patterns, detailed within the following desk.

Answer overview

For this submit, we use an instance of a temperature sensor community (element 1 within the following structure diagram) that emits sensor data, equivalent to temperature, sensor ID, standing, and the timestamp this occasion was produced. These temperature occasions get ingested into Amazon Kinesis Knowledge Streams (2). Downstream methods additionally require the model and nation code data of the sensors, to be able to analyze, for instance, the reliability per model and temperature per plant aspect.

Based mostly on the sensor ID, we enrich this sensor data from the Sensor Information API (3), which give us with data of the model, location, and a picture. The ensuing enriched stream is shipped to a different Kinesis information stream and may then be analyzed in an Amazon Managed Service for Apache Flink Studio pocket book (4).

Stipulations

To get began with implementing real-time information enrichment patterns, you’ll be able to clone or obtain the code from the GitHub repository. This repository implements the Flink streaming utility we described. Yow will discover the directions on methods to arrange Flink in both Amazon Managed Service for Apache Flink or different obtainable Flink deployment choices within the README.md file.

If you wish to find out how these patterns are applied and methods to optimize efficiency on your Flink utility, you’ll be able to merely comply with together with this submit with out deploying the samples.

Venture overview

The venture is structured as follows:

docs/                               -- Comprises venture documentation
src/
├── predominant/java/...                   -- Comprises all of the Flink utility code
│   ├── ProcessTemperatureStream    -- Predominant class that decides on the enrichment technique
│   ├── enrichment.                 -- Comprises the totally different enrichment methods (sync, async and cached)
│   ├── occasion.                      -- Occasion POJOs
│   ├── serialize.                  -- Utils for serialization
│   └── utils.                      -- Utils for Parameter parsing
└── check/                           -- Comprises all of the Flink testing code

The predominant methodology within the ProcessTemperatureStream class units up the run atmosphere and both takes the parameters from the command line, if it’s is an area atmosphere, or makes use of the applying properties from Amazon Managed Service for Apache Flink. Based mostly on the parameter EnrichmentStrategy, it decides which implementation to choose: synchronous enrichment (default), asynchronous enrichment, or cached enrichment based mostly on the Flink idea of KeyedState.

public static void predominant(String[] args) throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
     ParameterTool parameter = ParameterToolUtils.getParameters(args, env);

    String technique = parameter.get("EnrichmentStrategy", "SYNC");
     change (technique) CACHED)");
     
}

We go over the three approaches within the following sections.

Synchronous information enrichment

Once you need to enrich your information from an exterior supplier, you should use synchronous per-record lookup. When your Flink utility processes an incoming occasion, it makes an exterior HTTP name and after sending each request, it has to attend till it receives the response.

As Flink processes occasions synchronously, the thread that’s operating the enrichment is blocked till it receives the HTTP response. This ends in the processor staying idle for a major interval of processing time. Alternatively, the synchronous mannequin is less complicated to design, debug, and hint. It additionally lets you all the time have the newest information.

It may be built-in into your streaming utility as such:

DataStream<EnrichedTemperature> enrichedTemperatureDataStream =
        temperatureDataStream
                .map(new SyncEnrichmentFunction(parameter.get("SensorApiUrl", DEFAULT_API_URL)));

The implementation of the enrichment perform appears like the next code:

public class SyncEnrichmentFunction extends RichMapFunction<Temperature, EnrichedTemperature> {

    // Setup of HTTP consumer and ObjectMapper

    @Override
    public EnrichedTemperature map(Temperature temperature) throws Exception {
        String url = this.getRequestUrl + temperature.getSensorId();

        // Retrieve response from sensor data API
        Response response = consumer
                .prepareGet(url)
                .execute()
                .toCompletableFuture()
                .get();

        // Parse the sensor data
        SensorInfo sensorInfo = parseSensorInfo(response.getResponseBody());

        // Merge the temperature sensor information and sensor data information
        return getEnrichedTemperature(temperature, sensorInfo);
    }

    // ...
}

To optimize the efficiency for synchronous enrichment, you should use the KeepAlive flag as a result of the HTTP consumer might be reused for a number of occasions.

For functions with I/O-bound operators (equivalent to exterior information enrichment), it might additionally make sense to extend the applying parallelism with out growing the assets devoted to the applying. You are able to do this by growing the ParallelismPerKPU setting of the Amazon Managed Service for Apache Flink utility. This configuration describes the variety of parallel subtasks an utility can carry out per Kinesis Processing Unit (KPU), and a better worth of ParallelismPerKPU can result in full utilization of KPU assets. However take into account that growing the parallelism doesn’t work in all instances, equivalent to if you end up consuming from sources with few shards or partitions.

In our artificial testing with Amazon Managed Service for Apache Flink, we noticed a throughput of roughly 350 occasions per second on a single KPU with 4 parallelism per KPU and the default settings.

Asynchronous information enrichment

Synchronous enrichment doesn’t take full benefit of computing assets. That’s as a result of Fink waits for HTTP responses. However Flink affords asynchronous I/O for exterior information entry. This lets you enrich the stream occasions asynchronously, so it might ship a request for different parts within the stream whereas it waits for the response for the primary component and requests will be batched for higher effectivity.

Whereas utilizing this sample, it’s important to determine between unorderedWait (the place it emits the outcome to the following operator as quickly because the response is obtained, disregarding the order of the weather on the stream) and orderedWait (the place it waits till all inflight I/O operations full, then sends the outcomes to the following operator in the identical order as the unique parts had been positioned on the stream). When your use case doesn’t require occasion ordering, unorderedWait offers higher throughput and fewer idle time. Check with Enrich your information stream asynchronously utilizing Amazon Managed Service for Apache Flink to be taught extra about this sample.

The asynchronous enrichment will be added as follows:

SingleOutputStreamOperator<EnrichedTemperature> asyncEnrichedTemperatureSingleOutputStream =
        AsyncDataStream
                .unorderedWait(
                        temperatureDataStream,
                        new AsyncEnrichmentFunction(parameter.get("SensorApiUrl", DEFAULT_API_URL)),
                        ASYNC_OPERATOR_TIMEOUT,
                        TimeUnit.MILLISECONDS,
                        ASYNC_OPERATOR_CAPACITY);

The enrichment perform works related because the synchronous implementation. It first retrieves the sensor data as a Java Future, which represents the results of an asynchronous computation. As quickly because it’s obtainable, it parses the knowledge after which merges each objects into an EnrichedTemperature:

public class AsyncEnrichmentFunction extends RichAsyncFunction<Temperature, EnrichedTemperature> {

    // Setup of HTTP consumer and ObjectMapper

    @Override
    public void asyncInvoke(ultimate Temperature temperature, ultimate ResultFuture<EnrichedTemperature> resultFuture) {
        String url = this.getRequestUrl + temperature.getSensorId();

        // Retrieve response from sensor data API
        Future<Response> future = consumer
                .prepareGet(url)
                .execute();
        CompletableFuture
                .supplyAsync(() -> {
                    attempt {
                        Response response = future.get();

                        // Parse the sensor data as quickly as it's obtainable
                        return parseSensorInfo(response.getResponseBody());
                    } catch (Exception e) {
                        return null;
                    }
                })
                .thenAccept((SensorInfo sensorInfo) ->

                    // Merge the temperature sensor information and sensor data information
                    resultFuture.full(getEnrichedTemperature(temperature, sensorInfo)));
    }

    // ...
}

In our testing with Amazon Managed Service for Apache Flink, we noticed a throughput of two,000 occasions per second on a single KPU with 2 parallelism per KPU and the default settings.

Synchronous cached information enrichment

Though quite a few operations in a knowledge circulation give attention to particular person occasions independently, equivalent to occasion parsing, there are specific operations that retain data throughout a number of occasions. These operations, equivalent to window operators, are known as stateful as a consequence of their capability to take care of state.

The keyed state is saved inside an embedded key-value retailer, conceptualized as part of Flink’s structure. This state is partitioned and distributed together with the streams which can be consumed by the stateful operators. Because of this, entry to the key-value state is restricted to keyed streams, that means it might solely be accessed after a keyed or partitioned information trade, and is restricted to the values related to the present occasion’s key. For extra details about the ideas, discuss with Stateful Stream Processing.

You need to use the keyed state for regularly accessed data that doesn’t change usually, such because the sensor data. This won’t solely let you scale back the load on downstream assets, but additionally enhance the effectivity of your information enrichment as a result of no round-trip to an exterior useful resource for already fetched keys is critical and there’s additionally no must recompute the knowledge. However take into account that Amazon Managed Service for Apache Flink shops transient information in a RocksDB backend, which provides a latency to retrieving the knowledge. However as a result of RocksDB is native to the node processing the info, that is sooner than reaching out to exterior assets, as you’ll be able to see within the following instance.

To make use of keyed streams, it’s important to partition your stream utilizing the .keyBy(...) methodology, which assures that occasions for a similar key, on this case sensor ID, might be routed to the identical employee. You’ll be able to implement it as follows:

SingleOutputStreamOperator<EnrichedTemperature> cachedEnrichedTemperatureSingleOutputStream = temperatureDataStream
        .keyBy(Temperature::getSensorId)
        .course of(new CachedEnrichmentFunction(
                parameter.get("SensorApiUrl", DEFAULT_API_URL),
                parameter.get("CachedItemsTTL", String.valueOf(CACHED_ITEMS_TTL))));

We’re utilizing the sensor ID as the important thing to partition the stream and later enrich it. This fashion, we are able to then cache the sensor data as a part of the keyed state. When selecting a partition key on your use case, select one which has a excessive cardinality. This results in an excellent distribution of occasions throughout totally different staff.

To retailer the sensor data, we use the ValueState. To configure the state administration, we’ve to explain the state sort through the use of the TypeHint. Moreover, we are able to configure how lengthy a sure state might be cached by specifying the time-to-live (TTL) earlier than the state might be cleaned up and has to retrieved or recomputed once more.

public class CachedEnrichmentFunction extends KeyedProcessFunction<String, Temperature, EnrichedTemperature> {

    // Setup of HTTP consumer and ObjectMapper...

    non-public transient ValueState<SensorInfo> cachedSensorInfoLight;
    
    @Override
    public void open(Configuration configuration) throws Exception {
        // Initialize HTTP consumer
    
        ValueStateDescriptor<SensorInfo> descriptor =
                new ValueStateDescriptor<>("sensorInfo", TypeInformation.of(new TypeHint<>()}));
    
        StateTtlConfig ttlConfig = StateTtlConfig
                .newBuilder(Time.seconds(this.ttl))
                .setUpdateType(StateTtlConfig.UpdateType.OnCreateAndWrite)
                .setStateVisibility(StateTtlConfig.StateVisibility.ReturnExpiredIfNotCleanedUp)
                .construct();
        descriptor.enableTimeToLive(ttlConfig);
    
        cachedSensorInfoLight = getRuntimeContext().getState(descriptor);
    }
    
    // ...
}

As of Flink 1.17, entry to the state shouldn’t be doable in asynchronous capabilities, so the implementation have to be synchronous.

It first checks if the sensor data for this specific key exists; in that case, it will get enriched. In any other case, it retrieves the sensor data, parses it, after which merges each objects into an EnrichedTemperature:

public class CachedEnrichmentFunction extends KeyedProcessFunction<String, Temperature, EnrichedTemperature> {

    // Setup of HTTP consumer, ObjectMapper and ValueState

    @Override
    public void processElement(Temperature temperature, KeyedProcessFunction<String, Temperature, EnrichedTemperature>.Context ctx, Collector<EnrichedTemperature> out) throws Exception {
        SensorInfo sensorInfoCachedEntry = cachedSensorInfoLight.worth();

        // Verify if sensor data is cached
        if (sensorInfoCachedEntry != null) {
            out.acquire(getEnrichedTemperature(temperature, sensorInfoCachedEntry));
        } else {
            String url = this.getRequestUrl + temperature.getSensorId();

            // Retrieve response from sensor data API
            Response response = consumer
                    .prepareGet(url)
                    .execute()
                    .toCompletableFuture()
                    .get();

            // Parse the sensor data
            SensorInfo sensorInfo = parseSensorInfo(response.getResponseBody());

            // Cache the sensor data
            cachedSensorInfoLight.replace(sensorInfo);

            // Merge the temperature sensor information and sensor data information
            out.acquire(getEnrichedTemperature(temperature, sensorInfo));
        }
    }

    // ...
}

In our artificial testing with Amazon Managed Service for Apache Flink, we noticed a throughput of 28,000 occasions per second on a single KPU with 4 parallelism per KPU and the default settings.

You may as well see the influence and decreased load on the downstream sensor API.

Check your workload on Amazon Managed Service for Apache Flink

This submit in contrast totally different approaches to run an utility on Amazon Managed Service for Apache Flink with 1 KPU. Testing with a single KPU offers an excellent efficiency baseline that lets you evaluate the enrichment patterns with out producing a full-scale manufacturing workload.

It’s vital to know that the precise efficiency of the enrichment patterns relies on the precise workload and different exterior methods the Flink utility interacts with. For instance, efficiency of cached enrichment could differ with the cache hit ratio. Synchronous enrichment could behave in a different way relying on the response latency of the enrichment endpoint.

To guage which strategy most accurately fits your workload, you need to first carry out scaled-down exams with 1 KPU and a restricted throughput of practical information, probably experimenting with totally different values of Parallelism per KPU. After you determine the very best strategy, it’s vital to check the implementation at full scale, with actual information and integrating with actual exterior methods, earlier than transferring to manufacturing.

Abstract

This submit explored totally different approaches to implement real-time information enrichment utilizing Flink, specializing in three communication patterns: synchronous enrichment, asynchronous enrichment, and caching with Flink KeyedState.

We in contrast the throughput achieved by every strategy, with caching utilizing Flink KeyedState being as much as 14 instances sooner than utilizing asynchronous I/O, on this specific experiment with artificial information. Moreover, we delved into optimizing the efficiency of Apache Flink, particularly on Amazon Managed Service for Apache Flink. We mentioned methods and finest practices to maximise the efficiency of Flink functions in a managed atmosphere, enabling you to totally make the most of the capabilities of Flink on your real-time information enrichment wants.

Total, this overview affords insights into totally different information enrichment patterns, their efficiency traits, and optimization methods when utilizing Apache Flink, significantly within the context of real-time information enrichment eventualities and on Amazon Managed Service for Apache Flink.

We welcome your suggestions. Please go away your ideas and questions within the feedback part.

Concerning the authors

Luis Morales works as Senior Options Architect with digital-native companies to help them in always reinventing themselves within the cloud. He’s captivated with software program engineering, cloud-native distributed methods, test-driven growth, and all issues code and safety.

Lorenzo Nicora works as Senior Streaming Answer Architect serving to clients throughout EMEA. He has been constructing cloud-native, data-intensive methods for a number of years, working within the finance trade each by means of consultancies and for fin-tech product corporations. He leveraged open supply applied sciences extensively and contributed to a number of tasks, together with Apache Flink.