{"id":101003,"date":"2019-12-31T10:00:47","date_gmt":"2019-12-31T08:00:47","guid":{"rendered":"https:\/\/www.javacodegeeks.com\/?p=101003"},"modified":"2019-12-16T12:07:36","modified_gmt":"2019-12-16T10:07:36","slug":"popular-frameworks-for-big-data-processing-in-java","status":"publish","type":"post","link":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html","title":{"rendered":"Popular frameworks for big data processing in Java"},"content":{"rendered":"

The big data challenge<\/h2>\n

The concept of big data is understood differently in the variety of domains where companies face the need to deal with increasing volumes of data. In most of these scenarios the system under consideration needs to be designed in such a way so that it is capable of processing that data without sacrificing throughput as data grows in size. This essentially leads to the necessity of building systems that are highly scalable so that more resources can be allocated based on the volume of data that needs to be processed at a given point in time.<\/p>\n

Building such a system is a time-consuming and complex activity and for that reason a third-party frameworks and libraries can be used to provide the scalability requirements out of the box. There are already a number of good choices that can be used in Java applications and this article we will discuss briefly some of the most popular ones:<\/p>\n

\n
\"1-big<\/figure>\n<\/div>\n

The frameworks in action<\/h2>\n

We are going to demonstrate each of the frameworks by implementing a simple pipeline for processing of data from devices that measure the air quality index for a given area. For simplicity we will assume that numeric data from the devices is either received in batches or in a streaming fashion. Throughout the examples we are going to use the THRESHOLD constant to denote the value above which we consider an area being polluted.<\/p>\n

Apache Spark<\/h2>\n

In Spark we need to first convert the data into a proper format. We are going to use Datasets but we can also choose DataFrames or RDDs (Resilient Distributed Datasets) as an alternative for the data representation. We can then apply a number of Spark transformations and actions in order to process the data in a distributed fashion.<\/p>\n

\n
\n\n\n\n
\n
01<\/div>\n
02<\/div>\n
03<\/div>\n
04<\/div>\n
05<\/div>\n
06<\/div>\n
07<\/div>\n
08<\/div>\n
09<\/div>\n
10<\/div>\n
11<\/div>\n
12<\/div>\n
13<\/div>\n
14<\/div>\n
15<\/div>\n
16<\/div>\n
17<\/div>\n
18<\/div>\n<\/td>\n
\n
\n
public<\/code> long<\/code> countPollutedRegions(String[] numbers) {<\/code><\/div>\n
        <\/code>\/\/ runs a Spark master that takes up 4 cores<\/code><\/div>\n
        <\/code>SparkSession session = SparkSession.builder().<\/code><\/div>\n
                <\/code>appName(<\/code>\"AirQuality\"<\/code>).<\/code><\/div>\n
                <\/code>master(<\/code>\"local[4]\"<\/code>).<\/code><\/div>\n
                <\/code>getOrCreate();<\/code><\/div>\n
        <\/code>\/\/ converts the array of numbers to a Spark dataset<\/code><\/div>\n
        <\/code>Dataset numbersSet = session.createDataset(Arrays.asList(numbers), <\/code><\/div>\n
                <\/code>Encoders.STRING());<\/code><\/div>\n
        <\/code> <\/div>\n
        <\/code>\/\/ runs the data pipeline on the local spark<\/code><\/div>\n
        <\/code>long<\/code> pollutedRegions = numbersSet.map(number -> Integer.valueOf(number), <\/code><\/div>\n
                <\/code>Encoders.<\/code>INT<\/code>())<\/code><\/div>\n
                <\/code>.filter(number -> number > THRESHOLD).count();<\/code><\/div>\n
        <\/code> <\/div>\n
        <\/code> <\/div>\n
        <\/code>return<\/code> pollutedRegions;<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

If we want to change the above application to read data from an external source, write to an external data source and run it on a Spark cluster rather than a local Spark instance we would have the following execution flow:<\/p>\n

\n
\"2-big<\/figure>\n<\/div>\n

The Spark driver might be either a separate instance or part of the Spark cluster.<\/p>\n

Apache Flink<\/h2>\n

Similarly to Spark we need to represent the data in a Flink DataSet and then apply the necessary transformations and actions over it:<\/p>\n

\n
\n\n\n\n
\n
01<\/div>\n
02<\/div>\n
03<\/div>\n
04<\/div>\n
05<\/div>\n
06<\/div>\n
07<\/div>\n
08<\/div>\n
09<\/div>\n
10<\/div>\n
11<\/div>\n
12<\/div>\n
13<\/div>\n
14<\/div>\n
15<\/div>\n
16<\/div>\n
17<\/div>\n
18<\/div>\n<\/td>\n
\n
\n
public<\/code> long<\/code> countPollutedRegions(String[] numbers) throws Exception {<\/code><\/div>\n
        <\/code>\/\/ creates a Flink execution environment with proper configuration<\/code><\/div>\n
        <\/code>StreamExecutionEnvironment env = StreamExecutionEnvironment.<\/code><\/div>\n
                <\/code>createLocalEnvironment();<\/code><\/div>\n
 <\/div>\n
    <\/code>\/\/ converts the array of numbers to a Flink dataset and creates<\/code><\/div>\n
    <\/code>\/\/ the data pipiline<\/code><\/div>\n
        <\/code>DataStream stream = env.fromCollection(Arrays.asList(numbers)).<\/code><\/div>\n
                <\/code>map(number -> Integer.valueOf(number))<\/code><\/div>\n
                <\/code>.filter(number -> number > THRESHOLD).returns(Integer.<\/code>class<\/code>);<\/code><\/div>\n
        <\/code>long<\/code> pollutedRegions = 0;<\/code><\/div>\n
        <\/code>Iterator numbersIterator = DataStreamUtils.collect(stream);<\/code><\/div>\n
        <\/code>while<\/code>(numbersIterator.hasNext()) {<\/code><\/div>\n
            <\/code>pollutedRegions++;<\/code><\/div>\n
            <\/code>numbersIterator.next();<\/code><\/div>\n
        <\/code>}<\/code><\/div>\n
        <\/code>return<\/code> pollutedRegions;<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

If we want to change the above application to read data from an external source, write to an external data source and run it on a Flink cluster we would have the following execution flow:<\/p>\n

\n
\"3-big<\/figure>\n<\/div>\n

The Flink client where the application is submitted to the Flink cluster is either the Flink CLI utility or JobManager\u2019s UI.<\/p>\n

Apache Storm<\/h2>\n

In Storm the data pipeline is created as a topology of Spouts (the sources of data) and Bolts (the data processing units). Since Storm typically processes unbounded streams of data we will emulate the processing of an array of air quality index numbers as bounded stream:<\/p>\n

\n
\n\n\n\n
\n
01<\/div>\n
02<\/div>\n
03<\/div>\n
04<\/div>\n
05<\/div>\n
06<\/div>\n
07<\/div>\n
08<\/div>\n
09<\/div>\n
10<\/div>\n
11<\/div>\n
12<\/div>\n
13<\/div>\n
14<\/div>\n
15<\/div>\n
16<\/div>\n
17<\/div>\n
18<\/div>\n
19<\/div>\n
20<\/div>\n
21<\/div>\n
22<\/div>\n
23<\/div>\n<\/td>\n
\n
\n
public<\/code> void<\/code> countPollutedRegions(String[] numbers) throws Exception {<\/code><\/div>\n
 <\/div>\n
        <\/code>\/\/ builds the topology as a combination of spouts and bolts<\/code><\/div>\n
        <\/code>TopologyBuilder builder = <\/code>new<\/code> TopologyBuilder();<\/code><\/div>\n
        <\/code>builder.setSpout(<\/code>\"numbers-spout\"<\/code>, <\/code>new<\/code> StormAirQualitySpout(numbers));<\/code><\/div>\n
        <\/code>builder.setBolt(<\/code>\"number-bolt\"<\/code>, <\/code>new<\/code> StormAirQualityBolt()).<\/code><\/div>\n
            <\/code>shuffleGrouping(<\/code>\"numbers-spout\"<\/code>);<\/code><\/div>\n
        <\/code> <\/div>\n
        <\/code>\/\/ prepares Storm conf and along with the topology submits it for <\/code><\/div>\n
        <\/code>\/\/ execution to a local Storm cluster<\/code><\/div>\n
        <\/code>Config conf = <\/code>new<\/code> Config();<\/code><\/div>\n
        <\/code>conf.setDebug(<\/code>true<\/code>);<\/code><\/div>\n
        <\/code>LocalCluster localCluster = null;<\/code><\/div>\n
        <\/code>try<\/code> {<\/code><\/div>\n
            <\/code>localCluster = <\/code>new<\/code> LocalCluster();<\/code><\/div>\n
            <\/code>localCluster.submitTopology(<\/code>\"airquality-topology\"<\/code>, <\/code><\/div>\n
                    <\/code>conf, builder.createTopology());<\/code><\/div>\n
            <\/code>Thread.sleep(10000);<\/code><\/div>\n
            <\/code>localCluster.shutdown();<\/code><\/div>\n
        <\/code>} <\/code>catch<\/code> (InterruptedException ex) {<\/code><\/div>\n
            <\/code>localCluster.shutdown();<\/code><\/div>\n
        <\/code>}<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

We have one spout that provides a data source for the array of air quality index numbers and one bolt that filters only the ones that indicate polluted areas:<\/p>\n

\n
\n\n\n\n
\n
01<\/div>\n
02<\/div>\n
03<\/div>\n
04<\/div>\n
05<\/div>\n
06<\/div>\n
07<\/div>\n
08<\/div>\n
09<\/div>\n
10<\/div>\n
11<\/div>\n
12<\/div>\n
13<\/div>\n
14<\/div>\n
15<\/div>\n
16<\/div>\n
17<\/div>\n
18<\/div>\n
19<\/div>\n
20<\/div>\n
21<\/div>\n
22<\/div>\n
23<\/div>\n
24<\/div>\n
25<\/div>\n
26<\/div>\n
27<\/div>\n
28<\/div>\n
29<\/div>\n
30<\/div>\n
31<\/div>\n
32<\/div>\n
33<\/div>\n
34<\/div>\n
35<\/div>\n
36<\/div>\n<\/td>\n
\n
\n
public<\/code> class<\/code> StormAirQualitySpout extends BaseRichSpout {<\/code><\/div>\n
 <\/div>\n
    <\/code>private<\/code> boolean emitted = <\/code>false<\/code>;<\/code><\/div>\n
 <\/div>\n
    <\/code>private<\/code> SpoutOutputCollector collector;<\/code><\/div>\n
 <\/div>\n
    <\/code>private<\/code> String[] numbers;<\/code><\/div>\n
 <\/div>\n
    <\/code>public<\/code> StormAirQualitySpout(String[] numbers) {<\/code><\/div>\n
        <\/code>this<\/code>.numbers = numbers;<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n
    <\/code> <\/div>\n
    <\/code>@Override<\/code><\/div>\n
    <\/code>public<\/code> void<\/code> declareOutputFields(OutputFieldsDeclarer declarer) {<\/code><\/div>\n
        <\/code>declarer.declare(<\/code>new<\/code> Fields(<\/code>\"number\"<\/code>));<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n
 <\/div>\n
    <\/code>@Override<\/code><\/div>\n
    <\/code>public<\/code> void<\/code> open(Map paramas, <\/code><\/div>\n
            <\/code>TopologyContext context, <\/code><\/div>\n
            <\/code>SpoutOutputCollector collector) {<\/code><\/div>\n
        <\/code>this<\/code>.collector = collector;<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n
 <\/div>\n
    <\/code>@Override<\/code><\/div>\n
    <\/code>public<\/code> void<\/code> nextTuple() {<\/code><\/div>\n
        <\/code>\/\/ we make sure that the numbers array is processed just once by <\/code><\/div>\n
        <\/code>\/\/ the spout<\/code><\/div>\n
        <\/code>if<\/code>(!emitted) {<\/code><\/div>\n
            <\/code>for<\/code>(String number : numbers) {<\/code><\/div>\n
                <\/code>collector.emit(<\/code>new<\/code> Values(number));<\/code><\/div>\n
            <\/code>}<\/code><\/div>\n
            <\/code>emitted = <\/code>true<\/code>;<\/code><\/div>\n
        <\/code>}<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n
}<\/code><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
\n
\n\n\n\n
\n
01<\/div>\n
02<\/div>\n
03<\/div>\n
04<\/div>\n
05<\/div>\n
06<\/div>\n
07<\/div>\n
08<\/div>\n
09<\/div>\n
10<\/div>\n
11<\/div>\n
12<\/div>\n
13<\/div>\n
14<\/div>\n
15<\/div>\n
16<\/div>\n
17<\/div>\n
18<\/div>\n
19<\/div>\n
20<\/div>\n
21<\/div>\n
22<\/div>\n
23<\/div>\n
24<\/div>\n
25<\/div>\n
26<\/div>\n<\/td>\n
\n
\n
public<\/code> class<\/code> StormAirQualityBolt extends BaseRichBolt {<\/code><\/div>\n
 <\/div>\n
    <\/code>private<\/code> static<\/code> final<\/code> int<\/code> THRESHOLD = 10;<\/code><\/div>\n
 <\/div>\n
    <\/code>private<\/code> int<\/code> pollutedRegions = 0;<\/code><\/div>\n
 <\/div>\n
    <\/code>@Override<\/code><\/div>\n
    <\/code>public<\/code> void<\/code> declareOutputFields(OutputFieldsDeclarer declarer) {<\/code><\/div>\n
        <\/code>declarer.declare(<\/code>new<\/code> Fields(<\/code>\"number\"<\/code>));<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n
 <\/div>\n
    <\/code>@Override<\/code><\/div>\n
    <\/code>public<\/code> void<\/code> prepare(Map params, <\/code><\/div>\n
TopologyContext context, <\/code><\/div>\n
OutputCollector collector) {<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n
 <\/div>\n
    <\/code>@Override<\/code><\/div>\n
    <\/code>public<\/code> void<\/code> execute(Tuple tuple) {<\/code><\/div>\n
        <\/code>String number = tuple.getStringByField(<\/code>\"number\"<\/code>);<\/code><\/div>\n
        <\/code>Integer numberInt = Integer.valueOf(number);<\/code><\/div>\n
        <\/code>if<\/code> (numberInt > THRESHOLD) {<\/code><\/div>\n
            <\/code>pollutedRegions++;<\/code><\/div>\n
        <\/code>}<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n
}<\/code><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

We are using a LocalCluster instance for submitting to a local Storm cluster which is convenient for development purposes but we want to submit the Storm topology to a production cluster. In that case we would have the following execution flow:

<\/div> <\/div><\/p>\n
\n
\"4-big<\/figure>\n<\/div>\n

Apache Ignite<\/h2>\n

In Ignite we need first to put the data in the distributed cache before running the data processing pipeline which is the former of an SQL query executed in a distributed fashion over the Ignite cluster:<\/p>\n

\n
\n\n\n\n
\n
01<\/div>\n
02<\/div>\n
03<\/div>\n
04<\/div>\n
05<\/div>\n
06<\/div>\n
07<\/div>\n
08<\/div>\n
09<\/div>\n
10<\/div>\n
11<\/div>\n
12<\/div>\n
13<\/div>\n
14<\/div>\n
15<\/div>\n
16<\/div>\n
17<\/div>\n
18<\/div>\n
19<\/div>\n
20<\/div>\n
21<\/div>\n
22<\/div>\n
23<\/div>\n
24<\/div>\n
25<\/div>\n
26<\/div>\n
27<\/div>\n
28<\/div>\n
29<\/div>\n
30<\/div>\n
31<\/div>\n<\/td>\n
\n
\n
public<\/code> long<\/code> countPollutedRegions(String[] numbers) {<\/code><\/div>\n
 <\/div>\n
        <\/code>IgniteConfiguration igniteConfig = <\/code>new<\/code> IgniteConfiguration();<\/code><\/div>\n
        <\/code>CacheConfiguration cacheConfig = <\/code><\/div>\n
                <\/code>new<\/code> CacheConfiguration();<\/code><\/div>\n
        <\/code>\/\/ cache key is number index in the array and value is the number<\/code><\/div>\n
    <\/code>cacheConfig.setIndexedTypes(Integer.<\/code>class<\/code>, String.<\/code>class<\/code>);<\/code><\/div>\n
 <\/div>\n
        <\/code>cacheConfig.setName(NUMBERS_CACHE);<\/code><\/div>\n
        <\/code>igniteConfig.setCacheConfiguration(cacheConfig);<\/code><\/div>\n
        <\/code> <\/div>\n
        <\/code>try<\/code> (Ignite ignite = Ignition.start(igniteConfig)) {<\/code><\/div>\n
            <\/code>IgniteCache cache = ignite.getOrCreateCache(NUMBERS_CACHE);<\/code><\/div>\n
            <\/code>\/\/ adds the numbers to the Ignite cache<\/code><\/div>\n
            <\/code>try<\/code> (IgniteDataStreamer streamer = <\/code><\/div>\n
                    <\/code>ignite.dataStreamer(cache.getName())) {<\/code><\/div>\n
                <\/code>int<\/code> key = 0;<\/code><\/div>\n
                <\/code>for<\/code> (String number : numbers) {<\/code><\/div>\n
                    <\/code>streamer.addData(key++, number);<\/code><\/div>\n
                <\/code>}<\/code><\/div>\n
            <\/code>}<\/code><\/div>\n
 <\/div>\n
            <\/code>\/\/ performs an SQL query over the cached numbers<\/code><\/div>\n
            <\/code>SqlFieldsQuery query = <\/code>new<\/code> SqlFieldsQuery(<\/code>\"select * from String where _val > \"<\/code> + THRESHOLD);<\/code><\/div>\n
            <\/code> <\/div>\n
            <\/code>FieldsQueryCursor<List> cursor = cache.query(query);<\/code><\/div>\n
 <\/div>\n
            <\/code>int<\/code> pollutedRegions = cursor.getAll().size();<\/code><\/div>\n
            <\/code>return<\/code> pollutedRegions;<\/code><\/div>\n
        <\/code>}<\/code><\/div>\n
}<\/code><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

If we want to run the application in an Ignite cluster it will have the following execution flow:<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

Hazelcast Jet<\/h2>\n

Hazelcast Jet works on top of Hazelcast IMDG and similarly to Ignite if we want to process data we need first to put it in the Hazelcast IMDG cluster:<\/p>\n

\n
\n\n\n\n
\n
01<\/div>\n
02<\/div>\n
03<\/div>\n
04<\/div>\n
05<\/div>\n
06<\/div>\n
07<\/div>\n
08<\/div>\n
09<\/div>\n
10<\/div>\n
11<\/div>\n
12<\/div>\n
13<\/div>\n
14<\/div>\n
15<\/div>\n
16<\/div>\n
17<\/div>\n
18<\/div>\n
19<\/div>\n
20<\/div>\n
21<\/div>\n
22<\/div>\n
23<\/div>\n
24<\/div>\n
25<\/div>\n<\/td>\n
\n
\n
public<\/code> long<\/code> countPollutedRegions(String[] numbers) {<\/code><\/div>\n
 <\/div>\n
        <\/code>\/\/ prepares the Jet data processing pipeline<\/code><\/div>\n
        <\/code>Pipeline p = Pipeline.create();<\/code><\/div>\n
        <\/code>p.drawFrom(Sources.list(<\/code>\"numbers\"<\/code>)).<\/code><\/div>\n
            <\/code>map(number -> Integer.valueOf((String) number))<\/code><\/div>\n
            <\/code>.filter(number -> number > THRESHOLD).drainTo(Sinks.list(<\/code>\"filteredNumbers\"<\/code>));<\/code><\/div>\n
 <\/div>\n
        <\/code>JetInstance jet = Jet.newJetInstance();<\/code><\/div>\n
        <\/code>IList numbersList = jet.getList(<\/code>\"numbers\"<\/code>);<\/code><\/div>\n
        <\/code>numbersList.addAll(Arrays.asList(numbers));<\/code><\/div>\n
 <\/div>\n
        <\/code>try<\/code> {<\/code><\/div>\n
            <\/code>\/\/ submits the pipeline in the Jet cluster<\/code><\/div>\n
            <\/code>jet.newJob(p).join();<\/code><\/div>\n
 <\/div>\n
            <\/code>\/\/ gets the filtered data from Hazelcast IMDG<\/code><\/div>\n
            <\/code>List filteredRecordsList = jet.getList(<\/code>\"filteredNumbers\"<\/code>);<\/code><\/div>\n
            <\/code>int<\/code> pollutedRegions = filteredRecordsList.size();<\/code><\/div>\n
 <\/div>\n
            <\/code>return<\/code> pollutedRegions;<\/code><\/div>\n
        <\/code>} finally {<\/code><\/div>\n
            <\/code>Jet.shutdownAll();<\/code><\/div>\n
        <\/code>}<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

Note however that Jet also provides integration without of external data sources and data does not need to be stored in the IMDG cluster. You can also do the aggregation without first storing the data into a list (review the full example in Github that contains the improved version). Thanks to Jaromir and Can from Hazelcast engineering team for the valuable input.<\/p>\n

If we want to run the application in a Hazelcast Jet cluster it will have the following execution flow:<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

Kafka Streams<\/h2>\n

Kafka Streams is a client library that uses Kafka topics as sources and sinks for the data processing pipeline. To make use of the Kafka Streams library for our scenario we would be putting the air quality index numbers in a numbers<\/strong> Kafka topic:<\/p>\n

\n
\n\n\n\n
\n
01<\/div>\n
02<\/div>\n
03<\/div>\n
04<\/div>\n
05<\/div>\n
06<\/div>\n
07<\/div>\n
08<\/div>\n
09<\/div>\n
10<\/div>\n
11<\/div>\n
12<\/div>\n
13<\/div>\n
14<\/div>\n
15<\/div>\n
16<\/div>\n
17<\/div>\n
18<\/div>\n
19<\/div>\n
20<\/div>\n
21<\/div>\n
22<\/div>\n
23<\/div>\n
24<\/div>\n
25<\/div>\n
26<\/div>\n
27<\/div>\n
28<\/div>\n
29<\/div>\n
30<\/div>\n
31<\/div>\n
32<\/div>\n
33<\/div>\n
34<\/div>\n
35<\/div>\n
36<\/div>\n
37<\/div>\n
38<\/div>\n
39<\/div>\n
40<\/div>\n<\/td>\n
\n
\n
public<\/code> long<\/code> countPollutedRegions() {<\/code><\/div>\n
 <\/div>\n
        <\/code>List result = <\/code>new<\/code> LinkedList();<\/code><\/div>\n
    <\/code>\/\/ key\/value pairs contain string items<\/code><\/div>\n
        <\/code>final<\/code> Serde stringSerde = Serdes.String();<\/code><\/div>\n
 <\/div>\n
        <\/code>\/\/ prepares and runs the data processing pipeline<\/code><\/div>\n
        <\/code>final<\/code> StreamsBuilder builder = <\/code>new<\/code> StreamsBuilder();        <\/code><\/div>\n
        <\/code>builder.stream(<\/code>\"numbers\"<\/code>, Consumed.with(stringSerde, stringSerde))<\/code><\/div>\n
                <\/code>.map((key, value) -> <\/code>new<\/code> KeyValue(key, Integer.valueOf(value))).<\/code><\/div>\n
                    <\/code>filter((key, value) -> value > THRESHOLD)<\/code><\/div>\n
                <\/code>.foreach((key, value) -> {<\/code><\/div>\n
                    <\/code>result.add(value.toString());<\/code><\/div>\n
                <\/code>});<\/code><\/div>\n
    <\/code> <\/div>\n
        <\/code>final<\/code> Topology topology = builder.build();<\/code><\/div>\n
        <\/code>final<\/code> KafkaStreams streams = <\/code>new<\/code> KafkaStreams(topology, <\/code><\/div>\n
                <\/code>createKafkaStreamsConfiguration());<\/code><\/div>\n
        <\/code>streams.start();<\/code><\/div>\n
 <\/div>\n
        <\/code>try<\/code> {<\/code><\/div>\n
            <\/code>Thread.sleep(10000);<\/code><\/div>\n
        <\/code>} <\/code>catch<\/code> (InterruptedException e) {<\/code><\/div>\n
            <\/code>e.printStackTrace();<\/code><\/div>\n
        <\/code>}<\/code><\/div>\n
        <\/code>int<\/code> pollutedRegions = result.size();<\/code><\/div>\n
        <\/code>System.out.println(<\/code>\"Number of severely polluted regions: \"<\/code> + pollutedRegions);<\/code><\/div>\n
        <\/code>streams.close();<\/code><\/div>\n
        <\/code>return<\/code> pollutedRegions;<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n
 <\/div>\n
    <\/code>private<\/code> Properties createKafkaStreamsConfiguration() {<\/code><\/div>\n
        <\/code>Properties props = <\/code>new<\/code> Properties();<\/code><\/div>\n
        <\/code>props.put(StreamsConfig.APPLICATION_ID_CONFIG, <\/code>\"text-search-config\"<\/code>);<\/code><\/div>\n
        <\/code>props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, <\/code>\"localhost:9092\"<\/code>);<\/code><\/div>\n
        <\/code>props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());<\/code><\/div>\n
        <\/code>props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());<\/code><\/div>\n
 <\/div>\n
        <\/code>return<\/code> props;<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

We will have the following execution flow for our Kafka Stream application instances:<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

Pulsar Functions<\/h2>\n

Apache Pulsar Functions are lightweight compute processes that work in a serverless fashion along with an Apache Pulsar cluster. Assuming we are streaming our air quality index in a Pulsar cluster we can write a function to count the number of indexes that exceed the given threshold and write the result back to Pulsar as follows:<\/p>\n

\n
\n\n\n\n
\n
01<\/div>\n
02<\/div>\n
03<\/div>\n
04<\/div>\n
05<\/div>\n
06<\/div>\n
07<\/div>\n
08<\/div>\n
09<\/div>\n
10<\/div>\n
11<\/div>\n
12<\/div>\n
13<\/div>\n
14<\/div>\n
15<\/div>\n
16<\/div>\n<\/td>\n
\n
\n
public<\/code> class<\/code> PulsarFunctionsAirQualityApplication <\/code><\/div>\n
    <\/code>implements Function {<\/code><\/div>\n
 <\/div>\n
    <\/code>private<\/code> static<\/code> final<\/code> int<\/code> HIGH_THRESHOLD = 10;<\/code><\/div>\n
 <\/div>\n
    <\/code>@Override<\/code><\/div>\n
    <\/code>public<\/code> Void process(String input, Context context) throws Exception {<\/code><\/div>\n
        <\/code> <\/div>\n
        <\/code>int<\/code> number = Integer.valueOf(input);<\/code><\/div>\n
        <\/code> <\/div>\n
        <\/code>if<\/code>(number > HIGH_THRESHOLD) {<\/code><\/div>\n
            <\/code>context.incrCounter(<\/code>\"pollutedRegions\"<\/code>, 1);<\/code><\/div>\n
        <\/code>}<\/code><\/div>\n
        <\/code>return<\/code> null;<\/code><\/div>\n
    <\/code>}<\/code><\/div>\n
}<\/code><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

The execution flow of the function along with a Pulsar cluster is the following:<\/p>\n

\n
\"\"<\/figure>\n<\/div>\n

The Pulsar function can run either in the Pulsar cluster or as a separate application.<\/p>\n

Summary<\/h2>\n

In this article we reviewed briefly some of the most popular frameworks that can be used to implement big data processing systems in Java. Each of the presented frameworks is fairly big and deserves a separate article on its own. Although quite simple our air quality index data pipeline demonstrates the way these frameworks operate and you can use that as a basis for expanding your knowledge in each one of them that might be of further interest. You can review the complete code samples here<\/a>.<\/p>\n

\n\n\n\n
\n

Published on Java Code Geeks with permission by Martin Toshev, partner at our JCG program<\/a>. See the original article here: Popular frameworks for big data processing in Java<\/a><\/p>\n

Opinions expressed by Java Code Geeks contributors are their own.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

The big data challenge The concept of big data is understood differently in the variety of domains where companies face the need to deal with increasing volumes of data. In most of these scenarios the system under consideration needs to be designed in such a way so that it is capable of processing that data …<\/p>\n","protected":false},"author":11888,"featured_media":112,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[372],"class_list":["post-101003","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-enterprise-java","tag-big-data"],"yoast_head":"\nPopular frameworks for big data processing in Java - Java Code Geeks<\/title>\n<meta name=\"description\" content=\"Interested to learn about big data? Check our article presenting some popular frameworks for big data processing in Java with examples\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Popular frameworks for big data processing in Java - Java Code Geeks\" \/>\n<meta property=\"og:description\" content=\"Interested to learn about big data? Check our article presenting some popular frameworks for big data processing in Java with examples\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html\" \/>\n<meta property=\"og:site_name\" content=\"Java Code Geeks\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/javacodegeeks\" \/>\n<meta property=\"article:published_time\" content=\"2019-12-31T08:00:47+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.javacodegeeks.com\/wp-content\/uploads\/2012\/10\/enterprise-java-logo.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"150\" \/>\n\t<meta property=\"og:image:height\" content=\"150\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Martin Toshev\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@javacodegeeks\" \/>\n<meta name=\"twitter:site\" content=\"@javacodegeeks\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Martin Toshev\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"17 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html\"},\"author\":{\"name\":\"Martin Toshev\",\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/#\\\/schema\\\/person\\\/d6aa7d95e114929bd9da5d1a62db40e8\"},\"headline\":\"Popular frameworks for big data processing in Java\",\"datePublished\":\"2019-12-31T08:00:47+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html\"},\"wordCount\":1039,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.javacodegeeks.com\\\/wp-content\\\/uploads\\\/2012\\\/10\\\/enterprise-java-logo.jpg\",\"keywords\":[\"Big Data\"],\"articleSection\":[\"Enterprise Java\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html\",\"url\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html\",\"name\":\"Popular frameworks for big data processing in Java - Java Code Geeks\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.javacodegeeks.com\\\/wp-content\\\/uploads\\\/2012\\\/10\\\/enterprise-java-logo.jpg\",\"datePublished\":\"2019-12-31T08:00:47+00:00\",\"description\":\"Interested to learn about big data? Check our article presenting some popular frameworks for big data processing in Java with examples\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html#primaryimage\",\"url\":\"https:\\\/\\\/www.javacodegeeks.com\\\/wp-content\\\/uploads\\\/2012\\\/10\\\/enterprise-java-logo.jpg\",\"contentUrl\":\"https:\\\/\\\/www.javacodegeeks.com\\\/wp-content\\\/uploads\\\/2012\\\/10\\\/enterprise-java-logo.jpg\",\"width\":150,\"height\":150,\"caption\":\"java-interview-questions-answers\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/2019\\\/12\\\/popular-frameworks-for-big-data-processing-in-java.html#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.javacodegeeks.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Java\",\"item\":\"https:\\\/\\\/www.javacodegeeks.com\\\/category\\\/java\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Enterprise Java\",\"item\":\"https:\\\/\\\/www.javacodegeeks.com\\\/category\\\/java\\\/enterprise-java\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"Popular frameworks for big data processing in Java\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/#website\",\"url\":\"https:\\\/\\\/www.javacodegeeks.com\\\/\",\"name\":\"Java Code Geeks\",\"description\":\"Java Developers Resource Center\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/#organization\"},\"alternateName\":\"JCG\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.javacodegeeks.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/#organization\",\"name\":\"Exelixis Media P.C.\",\"url\":\"https:\\\/\\\/www.javacodegeeks.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/www.javacodegeeks.com\\\/wp-content\\\/uploads\\\/2022\\\/06\\\/exelixis-logo.png\",\"contentUrl\":\"https:\\\/\\\/www.javacodegeeks.com\\\/wp-content\\\/uploads\\\/2022\\\/06\\\/exelixis-logo.png\",\"width\":864,\"height\":246,\"caption\":\"Exelixis Media P.C.\"},\"image\":{\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/javacodegeeks\",\"https:\\\/\\\/x.com\\\/javacodegeeks\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/www.javacodegeeks.com\\\/#\\\/schema\\\/person\\\/d6aa7d95e114929bd9da5d1a62db40e8\",\"name\":\"Martin Toshev\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/a7ab09f89ce59abb723d92c2c46fff56b8b933ab8f6f81a0f69604ed6cfd4e4e?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/a7ab09f89ce59abb723d92c2c46fff56b8b933ab8f6f81a0f69604ed6cfd4e4e?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/a7ab09f89ce59abb723d92c2c46fff56b8b933ab8f6f81a0f69604ed6cfd4e4e?s=96&d=mm&r=g\",\"caption\":\"Martin Toshev\"},\"sameAs\":[\"https:\\\/\\\/www.javaadvent.com\"],\"url\":\"https:\\\/\\\/www.javacodegeeks.com\\\/author\\\/martin-toshev\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Popular frameworks for big data processing in Java - Java Code Geeks","description":"Interested to learn about big data? Check our article presenting some popular frameworks for big data processing in Java with examples","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html","og_locale":"en_US","og_type":"article","og_title":"Popular frameworks for big data processing in Java - Java Code Geeks","og_description":"Interested to learn about big data? Check our article presenting some popular frameworks for big data processing in Java with examples","og_url":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html","og_site_name":"Java Code Geeks","article_publisher":"https:\/\/www.facebook.com\/javacodegeeks","article_published_time":"2019-12-31T08:00:47+00:00","og_image":[{"width":150,"height":150,"url":"https:\/\/www.javacodegeeks.com\/wp-content\/uploads\/2012\/10\/enterprise-java-logo.jpg","type":"image\/jpeg"}],"author":"Martin Toshev","twitter_card":"summary_large_image","twitter_creator":"@javacodegeeks","twitter_site":"@javacodegeeks","twitter_misc":{"Written by":"Martin Toshev","Est. reading time":"17 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html#article","isPartOf":{"@id":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html"},"author":{"name":"Martin Toshev","@id":"https:\/\/www.javacodegeeks.com\/#\/schema\/person\/d6aa7d95e114929bd9da5d1a62db40e8"},"headline":"Popular frameworks for big data processing in Java","datePublished":"2019-12-31T08:00:47+00:00","mainEntityOfPage":{"@id":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html"},"wordCount":1039,"commentCount":0,"publisher":{"@id":"https:\/\/www.javacodegeeks.com\/#organization"},"image":{"@id":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html#primaryimage"},"thumbnailUrl":"https:\/\/www.javacodegeeks.com\/wp-content\/uploads\/2012\/10\/enterprise-java-logo.jpg","keywords":["Big Data"],"articleSection":["Enterprise Java"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html","url":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html","name":"Popular frameworks for big data processing in Java - Java Code Geeks","isPartOf":{"@id":"https:\/\/www.javacodegeeks.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html#primaryimage"},"image":{"@id":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html#primaryimage"},"thumbnailUrl":"https:\/\/www.javacodegeeks.com\/wp-content\/uploads\/2012\/10\/enterprise-java-logo.jpg","datePublished":"2019-12-31T08:00:47+00:00","description":"Interested to learn about big data? Check our article presenting some popular frameworks for big data processing in Java with examples","breadcrumb":{"@id":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html#primaryimage","url":"https:\/\/www.javacodegeeks.com\/wp-content\/uploads\/2012\/10\/enterprise-java-logo.jpg","contentUrl":"https:\/\/www.javacodegeeks.com\/wp-content\/uploads\/2012\/10\/enterprise-java-logo.jpg","width":150,"height":150,"caption":"java-interview-questions-answers"},{"@type":"BreadcrumbList","@id":"https:\/\/www.javacodegeeks.com\/2019\/12\/popular-frameworks-for-big-data-processing-in-java.html#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.javacodegeeks.com\/"},{"@type":"ListItem","position":2,"name":"Java","item":"https:\/\/www.javacodegeeks.com\/category\/java"},{"@type":"ListItem","position":3,"name":"Enterprise Java","item":"https:\/\/www.javacodegeeks.com\/category\/java\/enterprise-java"},{"@type":"ListItem","position":4,"name":"Popular frameworks for big data processing in Java"}]},{"@type":"WebSite","@id":"https:\/\/www.javacodegeeks.com\/#website","url":"https:\/\/www.javacodegeeks.com\/","name":"Java Code Geeks","description":"Java Developers Resource Center","publisher":{"@id":"https:\/\/www.javacodegeeks.com\/#organization"},"alternateName":"JCG","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.javacodegeeks.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.javacodegeeks.com\/#organization","name":"Exelixis Media P.C.","url":"https:\/\/www.javacodegeeks.com\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.javacodegeeks.com\/#\/schema\/logo\/image\/","url":"https:\/\/www.javacodegeeks.com\/wp-content\/uploads\/2022\/06\/exelixis-logo.png","contentUrl":"https:\/\/www.javacodegeeks.com\/wp-content\/uploads\/2022\/06\/exelixis-logo.png","width":864,"height":246,"caption":"Exelixis Media P.C."},"image":{"@id":"https:\/\/www.javacodegeeks.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/javacodegeeks","https:\/\/x.com\/javacodegeeks"]},{"@type":"Person","@id":"https:\/\/www.javacodegeeks.com\/#\/schema\/person\/d6aa7d95e114929bd9da5d1a62db40e8","name":"Martin Toshev","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/a7ab09f89ce59abb723d92c2c46fff56b8b933ab8f6f81a0f69604ed6cfd4e4e?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/a7ab09f89ce59abb723d92c2c46fff56b8b933ab8f6f81a0f69604ed6cfd4e4e?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/a7ab09f89ce59abb723d92c2c46fff56b8b933ab8f6f81a0f69604ed6cfd4e4e?s=96&d=mm&r=g","caption":"Martin Toshev"},"sameAs":["https:\/\/www.javaadvent.com"],"url":"https:\/\/www.javacodegeeks.com\/author\/martin-toshev"}]}},"_links":{"self":[{"href":"https:\/\/www.javacodegeeks.com\/wp-json\/wp\/v2\/posts\/101003","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.javacodegeeks.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.javacodegeeks.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.javacodegeeks.com\/wp-json\/wp\/v2\/users\/11888"}],"replies":[{"embeddable":true,"href":"https:\/\/www.javacodegeeks.com\/wp-json\/wp\/v2\/comments?post=101003"}],"version-history":[{"count":0,"href":"https:\/\/www.javacodegeeks.com\/wp-json\/wp\/v2\/posts\/101003\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.javacodegeeks.com\/wp-json\/wp\/v2\/media\/112"}],"wp:attachment":[{"href":"https:\/\/www.javacodegeeks.com\/wp-json\/wp\/v2\/media?parent=101003"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.javacodegeeks.com\/wp-json\/wp\/v2\/categories?post=101003"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.javacodegeeks.com\/wp-json\/wp\/v2\/tags?post=101003"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}