Spark Streaming: Avoid multiple calls to DB












0















I have a Spark structured streaming which reads UI events from a couple of busy Kafka topics.
Current flow is like this:




  1. Spark stream reads the Kafka offsets

  2. For every offset it goes to the database and maps one of values coming from the topic to another value.

  3. Aggregates the data

  4. Writes the data to the same database.


Facing this issue where after running 10-12 hours it throws too many db connection open error. It only does it for Step 2.



Using Aerospike Database for this Spark job.
Is there a way to optimize this flow? Is there calls to database can be reduced?



Read data:



sparkSession.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", kafkaBootstrapServersString)
.option("subscribe", newTopic)
.option("startingOffsets", "latest")
.option("enable.auto.commit", false)
.option("failOnDataLoss", false)
.load();


Map a value from database and aggregate data:



dataset
.map(
new MapFunction<Row, Row>() {
@Override
public Row call(Row row) throws Exception {
objects[1] = aerospikeDao.getSomeValueFromCode(row.getAs("code"));

return new GenericRowWithSchema(objects, eventSpecificStructType);
}
},
RowEncoder.apply(eventSpecificStructType)
)
.withWatermark("timestamp", "30 seconds")
.select(
col("timestamp"),
col("platform"),
col("some_value")
)
.groupBy(
functions.window(col("timestamp"), "30 seconds"),
col("platform"),
col("some_value")
)
.agg(
count(lit(1)).as("count")
);


Writing to the database:



aggregatedDataset
.writeStream()
.option("startingOffsets", "earliest")
.outputMode(OutputMode.Append())
.foreach(sink)
.trigger(Trigger.ProcessingTime("30 seconds"))
.start();


DAO:



    public AerospikeClient connect() {
if (aerospikeClient == null || !aerospikeClient.isConnected()) {
setAerospikeClient();
}
return this.aerospikeClient;
}

public void close() {
if (aerospikeClient != null && aerospikeClient.isConnected()) {
aerospikeClient.close();
}
}
public String getSomeValueFromCode(String code) {
connet();
Record record = aerospikeClient.get(Policy, key, "SomeValue");
close();
return channel;
}









share|improve this question

























  • Isn't that because some lagging micro batches are piling up?

    – user6910411
    Nov 23 '18 at 17:51
















0















I have a Spark structured streaming which reads UI events from a couple of busy Kafka topics.
Current flow is like this:




  1. Spark stream reads the Kafka offsets

  2. For every offset it goes to the database and maps one of values coming from the topic to another value.

  3. Aggregates the data

  4. Writes the data to the same database.


Facing this issue where after running 10-12 hours it throws too many db connection open error. It only does it for Step 2.



Using Aerospike Database for this Spark job.
Is there a way to optimize this flow? Is there calls to database can be reduced?



Read data:



sparkSession.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", kafkaBootstrapServersString)
.option("subscribe", newTopic)
.option("startingOffsets", "latest")
.option("enable.auto.commit", false)
.option("failOnDataLoss", false)
.load();


Map a value from database and aggregate data:



dataset
.map(
new MapFunction<Row, Row>() {
@Override
public Row call(Row row) throws Exception {
objects[1] = aerospikeDao.getSomeValueFromCode(row.getAs("code"));

return new GenericRowWithSchema(objects, eventSpecificStructType);
}
},
RowEncoder.apply(eventSpecificStructType)
)
.withWatermark("timestamp", "30 seconds")
.select(
col("timestamp"),
col("platform"),
col("some_value")
)
.groupBy(
functions.window(col("timestamp"), "30 seconds"),
col("platform"),
col("some_value")
)
.agg(
count(lit(1)).as("count")
);


Writing to the database:



aggregatedDataset
.writeStream()
.option("startingOffsets", "earliest")
.outputMode(OutputMode.Append())
.foreach(sink)
.trigger(Trigger.ProcessingTime("30 seconds"))
.start();


DAO:



    public AerospikeClient connect() {
if (aerospikeClient == null || !aerospikeClient.isConnected()) {
setAerospikeClient();
}
return this.aerospikeClient;
}

public void close() {
if (aerospikeClient != null && aerospikeClient.isConnected()) {
aerospikeClient.close();
}
}
public String getSomeValueFromCode(String code) {
connet();
Record record = aerospikeClient.get(Policy, key, "SomeValue");
close();
return channel;
}









share|improve this question

























  • Isn't that because some lagging micro batches are piling up?

    – user6910411
    Nov 23 '18 at 17:51














0












0








0








I have a Spark structured streaming which reads UI events from a couple of busy Kafka topics.
Current flow is like this:




  1. Spark stream reads the Kafka offsets

  2. For every offset it goes to the database and maps one of values coming from the topic to another value.

  3. Aggregates the data

  4. Writes the data to the same database.


Facing this issue where after running 10-12 hours it throws too many db connection open error. It only does it for Step 2.



Using Aerospike Database for this Spark job.
Is there a way to optimize this flow? Is there calls to database can be reduced?



Read data:



sparkSession.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", kafkaBootstrapServersString)
.option("subscribe", newTopic)
.option("startingOffsets", "latest")
.option("enable.auto.commit", false)
.option("failOnDataLoss", false)
.load();


Map a value from database and aggregate data:



dataset
.map(
new MapFunction<Row, Row>() {
@Override
public Row call(Row row) throws Exception {
objects[1] = aerospikeDao.getSomeValueFromCode(row.getAs("code"));

return new GenericRowWithSchema(objects, eventSpecificStructType);
}
},
RowEncoder.apply(eventSpecificStructType)
)
.withWatermark("timestamp", "30 seconds")
.select(
col("timestamp"),
col("platform"),
col("some_value")
)
.groupBy(
functions.window(col("timestamp"), "30 seconds"),
col("platform"),
col("some_value")
)
.agg(
count(lit(1)).as("count")
);


Writing to the database:



aggregatedDataset
.writeStream()
.option("startingOffsets", "earliest")
.outputMode(OutputMode.Append())
.foreach(sink)
.trigger(Trigger.ProcessingTime("30 seconds"))
.start();


DAO:



    public AerospikeClient connect() {
if (aerospikeClient == null || !aerospikeClient.isConnected()) {
setAerospikeClient();
}
return this.aerospikeClient;
}

public void close() {
if (aerospikeClient != null && aerospikeClient.isConnected()) {
aerospikeClient.close();
}
}
public String getSomeValueFromCode(String code) {
connet();
Record record = aerospikeClient.get(Policy, key, "SomeValue");
close();
return channel;
}









share|improve this question
















I have a Spark structured streaming which reads UI events from a couple of busy Kafka topics.
Current flow is like this:




  1. Spark stream reads the Kafka offsets

  2. For every offset it goes to the database and maps one of values coming from the topic to another value.

  3. Aggregates the data

  4. Writes the data to the same database.


Facing this issue where after running 10-12 hours it throws too many db connection open error. It only does it for Step 2.



Using Aerospike Database for this Spark job.
Is there a way to optimize this flow? Is there calls to database can be reduced?



Read data:



sparkSession.readStream()
.format("kafka")
.option("kafka.bootstrap.servers", kafkaBootstrapServersString)
.option("subscribe", newTopic)
.option("startingOffsets", "latest")
.option("enable.auto.commit", false)
.option("failOnDataLoss", false)
.load();


Map a value from database and aggregate data:



dataset
.map(
new MapFunction<Row, Row>() {
@Override
public Row call(Row row) throws Exception {
objects[1] = aerospikeDao.getSomeValueFromCode(row.getAs("code"));

return new GenericRowWithSchema(objects, eventSpecificStructType);
}
},
RowEncoder.apply(eventSpecificStructType)
)
.withWatermark("timestamp", "30 seconds")
.select(
col("timestamp"),
col("platform"),
col("some_value")
)
.groupBy(
functions.window(col("timestamp"), "30 seconds"),
col("platform"),
col("some_value")
)
.agg(
count(lit(1)).as("count")
);


Writing to the database:



aggregatedDataset
.writeStream()
.option("startingOffsets", "earliest")
.outputMode(OutputMode.Append())
.foreach(sink)
.trigger(Trigger.ProcessingTime("30 seconds"))
.start();


DAO:



    public AerospikeClient connect() {
if (aerospikeClient == null || !aerospikeClient.isConnected()) {
setAerospikeClient();
}
return this.aerospikeClient;
}

public void close() {
if (aerospikeClient != null && aerospikeClient.isConnected()) {
aerospikeClient.close();
}
}
public String getSomeValueFromCode(String code) {
connet();
Record record = aerospikeClient.get(Policy, key, "SomeValue");
close();
return channel;
}






java apache-spark spark-streaming spark-structured-streaming aerospike






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 23 '18 at 17:48









user6910411

34.4k1080104




34.4k1080104










asked Nov 23 '18 at 17:12









Himanshu YadavHimanshu Yadav

5,94634119223




5,94634119223













  • Isn't that because some lagging micro batches are piling up?

    – user6910411
    Nov 23 '18 at 17:51



















  • Isn't that because some lagging micro batches are piling up?

    – user6910411
    Nov 23 '18 at 17:51

















Isn't that because some lagging micro batches are piling up?

– user6910411
Nov 23 '18 at 17:51





Isn't that because some lagging micro batches are piling up?

– user6910411
Nov 23 '18 at 17:51












0






active

oldest

votes











Your Answer






StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});


}
});














draft saved

draft discarded


















StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53450678%2fspark-streaming-avoid-multiple-calls-to-db%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown

























0






active

oldest

votes








0






active

oldest

votes









active

oldest

votes






active

oldest

votes
















draft saved

draft discarded




















































Thanks for contributing an answer to Stack Overflow!


  • Please be sure to answer the question. Provide details and share your research!

But avoid



  • Asking for help, clarification, or responding to other answers.

  • Making statements based on opinion; back them up with references or personal experience.


To learn more, see our tips on writing great answers.




draft saved


draft discarded














StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53450678%2fspark-streaming-avoid-multiple-calls-to-db%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown





















































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown

































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown







Popular posts from this blog

Wiesbaden

Marschland

Dieringhausen