2. Kafka Streams Binder (2022)

2.Kafka Streams Binder

2.1Usage

For using the Kafka Streams binder, you just need to add it to your Spring Cloud Stream application, using the followingMaven coordinates:

<dependency> <groupId>org.springframework.cloud</groupId> <artifactId>spring-cloud-stream-binder-kafka-streams</artifactId></dependency>

2.2Overview

Spring Cloud Stream’s Apache Kafka support also includes a binder implementation designed explicitly for Apache KafkaStreams binding. With this native integration, a Spring Cloud Stream "processor" application can directly use theApache Kafka Streams APIs in the core business logic.

Kafka Streams binder implementation builds on the foundation provided by the Kafka Streams in Spring Kafkaproject.

Kafka Streams binder provides binding capabilities for the three major types in Kafka Streams - KStream, KTable and GlobalKTable.

As part of this native integration, the high-level Streams DSLprovided by the Kafka Streams API is available for use in the business logic.

An early version of the Processor APIsupport is available as well.

As noted early-on, Kafka Streams support in Spring Cloud Stream is strictly only available for use in the Processor model.A model in which the messages read from an inbound topic, business processing can be applied, and the transformed messagescan be written to an outbound topic. It can also be used in Processor applications with a no-outbound destination.

2.2.1Streams DSL

This application consumes data from a Kafka topic (e.g., words), computes word count for each unique word in a 5 secondstime window, and the computed results are sent to a downstream topic (e.g., counts) for further processing.

@SpringBootApplication@EnableBinding(KStreamProcessor.class)public class WordCountProcessorApplication {@StreamListener("input")@SendTo("output")public KStream<?, WordCount> process(KStream<?, String> input) {return input .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+"))) .groupBy((key, value) -> value) .windowedBy(TimeWindows.of(5000)) .count(Materialized.as("WordCounts-multi")) .toStream() .map((key, value) -> new KeyValue<>(null, new WordCount(key.key(), value, new Date(key.window().start()), new Date(key.window().end())))); }public static void main(String[] args) {SpringApplication.run(WordCountProcessorApplication.class, args);}

Once built as a uber-jar (e.g., wordcount-processor.jar), you can run the above example like the following.

java -jar wordcount-processor.jar --spring.cloud.stream.bindings.input.destination=words --spring.cloud.stream.bindings.output.destination=counts

This application will consume messages from the Kafka topic words and the computed results are published to an outputtopic counts.

Spring Cloud Stream will ensure that the messages from both the incoming and outgoing topics are automatically bound asKStream objects. As a developer, you can exclusively focus on the business aspects of the code, i.e. writing the logicrequired in the processor. Setting up the Streams DSL specific configuration required by the Kafka Streams infrastructureis automatically handled by the framework.

2.3Configuration Options

This section contains the configuration options used by the Kafka Streams binder.

For common configuration options and properties pertaining to binder, refer to the core documentation.

2.3.1Kafka Streams Properties

The following properties are available at the binder level and must be prefixed with spring.cloud.stream.kafka.streams.binder.

configuration
Map with a key/value pair containing properties pertaining to Apache Kafka Streams API.This property must be prefixed with spring.cloud.stream.kafka.streams.binder..Following are some examples of using this property.
(Video) Spring Cloud Stream With Apache Kafka Binder | Example | JavaTechie
spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde=org.apache.kafka.common.serialization.Serdes$StringSerdespring.cloud.stream.kafka.streams.binder.configuration.default.value.serde=org.apache.kafka.common.serialization.Serdes$StringSerdespring.cloud.stream.kafka.streams.binder.configuration.commit.interval.ms=1000

For more information about all the properties that may go into streams configuration, see StreamsConfig JavaDocs inApache Kafka Streams docs.

brokers

Broker URL

Default: localhost

zkNodes

Zookeeper URL

Default: localhost

serdeError

Deserialization error handler type.Possible values are - logAndContinue, logAndFail or sendToDlq

Default: logAndFail

applicationId

Convenient way to set the application.id for the Kafka Streams application globally at the binder level.If the application contains multiple StreamListener methods, then application.id should be set at the binding level per input binding.

Default: none

The following properties are only available for Kafka Streams producers and must be prefixed with spring.cloud.stream.kafka.streams.bindings.<binding name>.producer.For convenience, if there multiple output bindings and they all require a common value, that can be configured by using the prefix spring.cloud.stream.kafka.streams.default.producer..

keySerde

key serde to use

Default: none.

valueSerde

value serde to use

Default: none.

useNativeEncoding

flag to enable native encoding

Default: false.

The following properties are available for Kafka Streams consumers and must be prefixed with spring.cloud.stream.kafka.streams.bindings.<binding-name>.consumer.For convenience, if there are multiple input bindings and they all require a common value, that can be configured by using the prefix spring.cloud.stream.kafka.streams.default.consumer..

applicationId

Setting application.id per input binding.

Default: none

keySerde

key serde to use

Default: none.

valueSerde

value serde to use

Default: none.

materializedAs

state store to materialize when using incoming KTable types

Default: none.

useNativeDecoding

flag to enable native decoding

Default: false.

dlqName

DLQ topic name.

Default: none.

startOffset

Offset to start from if there is no committed offset to consume from.This is mostly used when the consumer is consuming from a topic for the first time. Kafka Streams uses earliest as the default strategy andthe binder uses the same default. This can be overridden to latest using this property.

Default: earliest.

Note: Using resetOffsets on the consumer does not have any effect on Kafka Streams binder.Unlike the message channel based binder, Kafka Streams binder does not seek to beginning or end on demand.

2.3.2TimeWindow properties:

(Video) Spring Cloud Stream with Apache Kafka and Rabbitmq binder | Spring Boot

Windowing is an important concept in stream processing applications. Following properties are available to configuretime-window computations.

spring.cloud.stream.kafka.streams.timeWindow.length

When this property is given, you can autowire a TimeWindows bean into the application.The value is expressed in milliseconds.

Default: none.

spring.cloud.stream.kafka.streams.timeWindow.advanceBy

Value is given in milliseconds.

Default: none.

2.4Multiple Input Bindings

For use cases that requires multiple incoming KStream objects or a combination of KStream and KTable objects, the KafkaStreams binder provides multiple bindings support.

Let’s see it in action.

2.4.1Multiple Input Bindings as a Sink

@EnableBinding(KStreamKTableBinding.class)..........@StreamListenerpublic void process(@Input("inputStream") KStream<String, PlayEvent> playEvents, @Input("inputTable") KTable<Long, Song> songTable) { .... ....}interface KStreamKTableBinding { @Input("inputStream") KStream<?, ?> inputStream(); @Input("inputTable") KTable<?, ?> inputTable();}

In the above example, the application is written as a sink, i.e. there are no output bindings and the application has todecide concerning downstream processing. When you write applications in this style, you might want to send the informationdownstream or store them in a state store (See below for Queryable State Stores).

In the case of incoming KTable, if you want to materialize the computations to a state store, you have to express itthrough the following property.

spring.cloud.stream.kafka.streams.bindings.inputTable.consumer.materializedAs: all-songs

The above example shows the use of KTable as an input binding.The binder also supports input bindings for GlobalKTable.GlobalKTable binding is useful when you have to ensure that all instances of your application has access to the data updates from the topic.KTable and GlobalKTable bindings are only available on the input.Binder supports both input and output bindings for KStream.

2.5Multiple Input Bindings as a Processor

@EnableBinding(KStreamKTableBinding.class)........@StreamListener@SendTo("output")public KStream<String, Long> process(@Input("input") KStream<String, Long> userClicksStream, @Input("inputTable") KTable<String, String> userRegionsTable) {........}interface KStreamKTableBinding extends KafkaStreamsProcessor { @Input("inputX") KTable<?, ?> inputTable();}

2.6Multiple Output Bindings (aka Branching)

Kafka Streams allow outbound data to be split into multiple topics based on some predicates. The Kafka Streams binder providessupport for this feature without compromising the programming model exposed through StreamListener in the end user application.

You can write the application in the usual way as demonstrated above in the word count example. However, when using thebranching feature, you are required to do a few things. First, you need to make sure that your return type is KStream[]instead of a regular KStream. Second, you need to use the SendTo annotation containing the output bindings in the order(see example below). For each of these output bindings, you need to configure destination, content-type etc., complying withthe standard Spring Cloud Stream expectations.

Here is an example:

@EnableBinding(KStreamProcessorWithBranches.class)@EnableAutoConfigurationpublic static class WordCountProcessorApplication { @Autowired private TimeWindows timeWindows; @StreamListener("input") @SendTo({"output1","output2","output3}) public KStream<?, WordCount>[] process(KStream<Object, String> input) {Predicate<Object, WordCount> isEnglish = (k, v) -> v.word.equals("english");Predicate<Object, WordCount> isFrench = (k, v) -> v.word.equals("french");Predicate<Object, WordCount> isSpanish = (k, v) -> v.word.equals("spanish");return input.flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+"))).groupBy((key, value) -> value).windowedBy(timeWindows).count(Materialized.as("WordCounts-1")).toStream().map((key, value) -> new KeyValue<>(null, new WordCount(key.key(), value, new Date(key.window().start()), new Date(key.window().end())))).branch(isEnglish, isFrench, isSpanish); } interface KStreamProcessorWithBranches { @Input("input") KStream<?, ?> input(); @Output("output1") KStream<?, ?> output1(); @Output("output2") KStream<?, ?> output2(); @Output("output3") KStream<?, ?> output3(); }}

Properties:

spring.cloud.stream.bindings.output1.contentType: application/jsonspring.cloud.stream.bindings.output2.contentType: application/jsonspring.cloud.stream.bindings.output3.contentType: application/jsonspring.cloud.stream.kafka.streams.binder.configuration.commit.interval.ms: 1000spring.cloud.stream.kafka.streams.binder.configuration: default.key.serde: org.apache.kafka.common.serialization.Serdes$StringSerde default.value.serde: org.apache.kafka.common.serialization.Serdes$StringSerdespring.cloud.stream.bindings.output1: destination: foo producer: headerMode: rawspring.cloud.stream.bindings.output2: destination: bar producer: headerMode: rawspring.cloud.stream.bindings.output3: destination: fox producer: headerMode: rawspring.cloud.stream.bindings.input: destination: words consumer: headerMode: raw

2.7Record Value Conversion

Kafka Streams binder can marshal producer/consumer values based on a content type and the converters provided out of the box in Spring Cloud Stream.

It is typical for Kafka Streams applications to provide Serde classes.Therefore, it may be more natural to rely on the SerDe facilities provided by the Apache Kafka Streams library itself for data conversion on inbound and outboundrather than rely on the content-type conversions offered by the binder.On the other hand, you might be already familiar with the content-type conversion patterns provided by Spring Cloud Stream andwould like to continue using that for inbound and outbound conversions.

(Video) Kafka Streams, Spring Boot, and Confluent Cloud | Livestreams 002

Both the options are supported in the Kafka Streams binder implementation. See below for more details.

2.7.1Outbound serialization

If native encoding is disabled (which is the default), then the framework will convert the message using the contentTypeset by the user (otherwise, the default application/json will be applied). It will ignore any SerDe set on the outboundin this case for outbound serialization.

Here is the property to set the contentType on the outbound.

spring.cloud.stream.bindings.output.contentType: application/json

Here is the property to enable native encoding.

spring.cloud.stream.bindings.output.nativeEncoding: true

If native encoding is enabled on the output binding (user has to enable it as above explicitly), then the framework willskip any form of automatic message conversion on the outbound. In that case, it will switch to the Serde set by the user.The valueSerde property set on the actual output binding will be used. Here is an example.

spring.cloud.stream.kafka.streams.bindings.output.producer.valueSerde: org.apache.kafka.common.serialization.Serdes$StringSerde

If this property is not set, then it will use the "default" SerDe: spring.cloud.stream.kafka.streams.binder.configuration.default.value.serde.

It is worth to mention that Kafka Streams binder does not serialize the keys on outbound - it simply relies on Kafka itself.Therefore, you either have to specify the keySerde property on the binding or it will default to the application-wide commonkeySerde.

Binding level key serde:

spring.cloud.stream.kafka.streams.bindings.output.producer.keySerde

Common Key serde:

spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde

If branching is used, then you need to use multiple output bindings. For example,

interface KStreamProcessorWithBranches { @Input("input") KStream<?, ?> input(); @Output("output1") KStream<?, ?> output1(); @Output("output2") KStream<?, ?> output2(); @Output("output3") KStream<?, ?> output3(); }

If nativeEncoding is set, then you can set different SerDe’s on individual output bindings as below.

spring.cloud.stream.kafka.streams.bindings.output1.producer.valueSerde=IntegerSerdespring.cloud.stream.kafka.streams.bindings.output2.producer.valueSerde=StringSerdespring.cloud.stream.kafka.streams.bindings.output3.producer.valueSerde=JsonSerde

Then if you have SendTo like this, @SendTo({"output1", "output2", "output3"}), the KStream[] from the branches areapplied with proper SerDe objects as defined above. If you are not enabling nativeEncoding, you can then set differentcontentType values on the output bindings as below. In that case, the framework will use the appropriate message converterto convert the messages before sending to Kafka.

spring.cloud.stream.bindings.output1.contentType: application/jsonspring.cloud.stream.bindings.output2.contentType: application/java-serialzied-objectspring.cloud.stream.bindings.output3.contentType: application/octet-stream

2.7.2Inbound Deserialization

Similar rules apply to data deserialization on the inbound.

If native decoding is disabled (which is the default), then the framework will convert the message using the contentTypeset by the user (otherwise, the default application/json will be applied). It will ignore any SerDe set on the inboundin this case for inbound deserialization.

Here is the property to set the contentType on the inbound.

spring.cloud.stream.bindings.input.contentType: application/json

Here is the property to enable native decoding.

spring.cloud.stream.bindings.input.nativeDecoding: true

If native decoding is enabled on the input binding (user has to enable it as above explicitly), then the framework willskip doing any message conversion on the inbound. In that case, it will switch to the SerDe set by the user. The valueSerdeproperty set on the actual output binding will be used. Here is an example.

spring.cloud.stream.kafka.streams.bindings.input.consumer.valueSerde: org.apache.kafka.common.serialization.Serdes$StringSerde

If this property is not set, it will use the default SerDe: spring.cloud.stream.kafka.streams.binder.configuration.default.value.serde.

It is worth to mention that Kafka Streams binder does not deserialize the keys on inbound - it simply relies on Kafka itself.Therefore, you either have to specify the keySerde property on the binding or it will default to the application-wide commonkeySerde.

Binding level key serde:

spring.cloud.stream.kafka.streams.bindings.input.consumer.keySerde

Common Key serde:

spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde

As in the case of KStream branching on the outbound, the benefit of setting value SerDe per binding is that if you havemultiple input bindings (multiple KStreams object) and they all require separate value SerDe’s, then you can configurethem individually. If you use the common configuration approach, then this feature won’t be applicable.

2.8Error Handling

Apache Kafka Streams provide the capability for natively handling exceptions from deserialization errors.For details on this support, please see thisOut of the box, Apache Kafka Streams provide two kinds of deserialization exception handlers - logAndContinue and logAndFail.As the name indicates, the former will log the error and continue processing the next records and the latter will log theerror and fail. LogAndFail is the default deserialization exception handler.

2.9Handling Deserialization Exceptions

(Video) Part 2 Even Driven Processing Spring Cloud Streams Functions Kafka Streams

Kafka Streams binder supports a selection of exception handlers through the following properties.

spring.cloud.stream.kafka.streams.binder.serdeError: logAndContinue

In addition to the above two deserialization exception handlers, the binder also provides a third one for sending the erroneousrecords (poison pills) to a DLQ topic. Here is how you enable this DLQ exception handler.

spring.cloud.stream.kafka.streams.binder.serdeError: sendToDlq

When the above property is set, all the deserialization error records are automatically sent to the DLQ topic.

spring.cloud.stream.kafka.streams.bindings.input.consumer.dlqName: foo-dlq

If this is set, then the error records are sent to the topic foo-dlq. If this is not set, then it will create a DLQtopic with the name error.<input-topic-name>.<group-name>.

A couple of things to keep in mind when using the exception handling feature in Kafka Streams binder.

  • The property spring.cloud.stream.kafka.streams.binder.serdeError is applicable for the entire application. This impliesthat if there are multiple StreamListener methods in the same application, this property is applied to all of them.
  • The exception handling for deserialization works consistently with native deserialization and framework provided messageconversion.

2.9.1Handling Non-Deserialization Exceptions

For general error handling in Kafka Streams binder, it is up to the end user applications to handle application level errors.As a side effect of providing a DLQ for deserialization exception handlers, Kafka Streams binder provides a way to getaccess to the DLQ sending bean directly from your application.Once you get access to that bean, you can programmatically send any exception records from your application to the DLQ.

It continues to remain hard to robust error handling using the high-level DSL; Kafka Streams doesn’t natively support errorhandling yet.

However, when you use the low-level Processor API in your application, there are options to control this behavior. Seebelow.

@Autowiredprivate SendToDlqAndContinue dlqHandler;@StreamListener("input")@SendTo("output")public KStream<?, WordCount> process(KStream<Object, String> input) { input.process(() -> new Processor() { ProcessorContext context; @Override public void init(ProcessorContext context) { this.context = context; } @Override public void process(Object o, Object o2) { try { ..... ..... } catch(Exception e) { //explicitly provide the kafka topic corresponding to the input binding as the first argument. //DLQ handler will correctly map to the dlq topic from the actual incoming destination. dlqHandler.sendToDlq("topic-name", (byte[]) o1, (byte[]) o2, context.partition()); } } ..... ..... });}

2.10State Store

State store is created automatically by Kafka Streams when the DSL is used.When processor API is used, you need to register a state store manually. In order to do so, you can use KafkaStreamsStateStore annotation.You can specify the name and type of the store, flags to control log and disabling cache, etc.Once the store is created by the binder during the bootstrapping phase, you can access this state store through the processor API.Below are some primitives for doing this.

Creating a state store:

@KafkaStreamsStateStore(name="mystate", type= KafkaStreamsStateStoreProperties.StoreType.WINDOW, lengthMs=300000)public void process(KStream<Object, Product> input) { ...}

Accessing the state store:

Processor<Object, Product>() { WindowStore<Object, String> state; @Override public void init(ProcessorContext processorContext) { state = (WindowStore)processorContext.getStateStore("mystate"); } ...}

2.11Interactive Queries

As part of the public Kafka Streams binder API, we expose a class called InteractiveQueryService.You can access this as a Spring bean in your application. An easy way to get access to this bean from your application is to "autowire" the bean.

@Autowiredprivate InteractiveQueryService interactiveQueryService;

Once you gain access to this bean, then you can query for the particular state-store that you are interested. See below.

ReadOnlyKeyValueStore<Object, Object> keyValueStore =interactiveQueryService.getQueryableStoreType("my-store", QueryableStoreTypes.keyValueStore());

If there are multiple instances of the kafka streams application running, then before you can query them interactively, you need to identify which application instance hosts the key.InteractiveQueryService API provides methods for identifying the host information.

In order for this to work, you must configure the property application.server as below:

spring.cloud.stream.kafka.streams.binder.configuration.application.server: <server>:<port>

Here are some code snippets:

org.apache.kafka.streams.state.HostInfo hostInfo = interactiveQueryService.getHostInfo("store-name",key, keySerializer);if (interactiveQueryService.getCurrentHostInfo().equals(hostInfo)) { //query from the store that is locally available}else { //query from the remote host}

2.12Accessing the underlying KafkaStreams object

StreamBuilderFactoryBean from spring-kafka that is responsible for constructing the KafkaStreams object can be accessed programmatically.Each StreamBuilderFactoryBean is registered as stream-builder and appended with the StreamListener method name.If your StreamListener method is named as process for example, the stream builder bean is named as stream-builder-process.Since this is a factory bean, it should be accessed by prepending an ampersand (&) when accessing it programmatically.Following is an example and it assumes the StreamListener method is named as process

StreamsBuilderFactoryBean streamsBuilderFactoryBean = context.getBean("&stream-builder-process", StreamsBuilderFactoryBean.class);KafkaStreams kafkaStreams = streamsBuilderFactoryBean.getKafkaStreams();

2.13State Cleanup

(Video) Spring Cloud Stream Binder for Apache Kafka QA | Livestreams 021

By default, the Kafkastreams.cleanup() method is called when the binding is stopped.See the Spring Kafka documentation.To modify this behavior simply add a single CleanupConfig @Bean (configured to clean up on start, stop, or neither) to the application context; the bean will be detected and wired into the factory bean.

FAQs

What is a Kafka binder? ›

The Apache Kafka Binder implementation maps each destination to an Apache Kafka topic. The consumer group maps directly to the same Apache Kafka concept. Partitioning also maps directly to Apache Kafka partitions as well.

What is spring cloud stream binder Kafka? ›

Spring Cloud Stream is a framework for building highly scalable event-driven microservices connected with shared messaging systems.

What is the difference between Kafka and Kafka Streams? ›

Difference between Kafka Streams and Kafka Consumer

Kafka Streams is an easy data processing and transformation library within Kafka used as a messaging service. Whereas, Kafka Consumer API allows applications to process messages from topics.

What is KStream in Kafka? ›

KStream is an abstraction of a record stream of KeyValue pairs, i.e., each record is an independent entity/event in the real world. For example a user X might buy two items I1 and I2, and thus there might be two records <K:I1>, <K:I2> in the stream.

Why are there 3 brokers in Kafka? ›

In addition to @hqt answer: You can setup a Kafka HA Cluster with only 2 brokers, but the recommended replication-factor for production is 3, so you need 3 brokers in order to achieve this.

How many Kafka partitions do I need? ›

For most implementations you want to follow the rule of thumb of 10 partitions per topic, and 10,000 partitions per Kafka cluster. Going beyond that amount can require additional monitoring and optimization. (You can learn more about Kafka monitoring here.)

Why use Kafka Streams over Kafka? ›

2.2.

Kafka Streams greatly simplifies the stream processing from topics. Built on top of Kafka client libraries, it provides data parallelism, distributed coordination, fault tolerance, and scalability.

What is the difference between spring and stream? ›

Answer. A spring is water coming from under the ground to the surface of the earth and a stream is water that is running along the ground through a trench like place on earth down a hill or steep a area.

What is Kafka Streams and ksqlDB? ›

Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in an Apache Kafka® cluster. ksqlDB is the streaming SQL engine for Kafka that you can use to perform stream processing tasks using SQL statements.

Is Kafka streams multithreaded? ›

Here is the anatomy of an application that uses the Kafka Streams API. It provides a logical view of a Kafka Streams application that contains multiple stream threads, that each contain multiple stream tasks.

Does Netflix use Kafka? ›

Apache Kafka is an open-source streaming platform that enables the development of applications that ingest a high volume of real-time data. It was originally built by the geniuses at LinkedIn and is now used at Netflix, Pinterest and Airbnb to name a few.

Is Kafka stream push or pull? ›

Since Kafka is pull-based, it implements aggressive batching of data.

What is the difference between stream and IntStream? ›

Stream<Integer> operates on boxed values ( Integer instead of primitive int) which takes significantly more memory and usually a lot of boxing/unboxing operations (depending on your code), whereas IntStream works with primitives.

Is Kafka a queue or stream? ›

We can use Kafka as a Message Queue or a Messaging System but as a distributed streaming platform Kafka has several other usages for stream processing or storing data. We can use Apache Kafka as: Messaging System: a highly scalable, fault-tolerant and distributed Publish/Subscribe messaging system.

What is the use of ksqlDB? ›

ksqlDB allows you to seamlessly integrate stream processing functionality onto an existing Kafka cluster with an interface as familiar as a relational database. It is also valuable in its ease of use for diverse development teams (Python, Go, and . NET), given that it speaks language-neutral SQL.

Does Kafka 3 require ZooKeeper? ›

In Kafka architecture, Zookeeper serves as a centralized controller for managing all the metadata information about Kafka producers, brokers, and consumers. However, you can install and run Kafka without Zookeeper.

Can Kafka have multiple brokers? ›

A Kafka cluster is a cluster which is composed of multiple brokers with their respective partitions.

Why Kafka brokers are stateless? ›

Kafka brokers are stateless, so they use ZooKeeper for maintaining their cluster state. One Kafka broker instance can handle hundreds of thousands of reads and writes per second and each bro-ker can handle TB of messages without performance impact. Kafka broker leader election can be done by ZooKeeper.

Is it better to have 2 partitions or 1? ›

2. if you just backup data, have two partitions–one for Windows and installed application programs (usually C:), the other for data (usually D:). Except for those running multiple operating systems, there is seldom any benefit to having more than two partitions.

What happens if Kafka partition is full? ›

Often the data will stay there and get deleted once a specified retention period or max size/data limit has been reached.

How many partitions is best for 1TB? ›

How many partitions are best for 1TB? 1TB hard drive can be partitioned into 2-5 partitions. Here we recommend you to partition it into four partitions: Operating system (C Drive), Program File(D Drive), Personal Data (E Drive), and Entertainment (F Drive).

When should you not use Kafka Streams? ›

As point 1 if having just a producer producing message we don't need Kafka Stream. If consumer messages from one Kafka cluster but publish to different Kafka cluster topics. In that case, you can even use Kafka Stream but have to use a separate Producer to publish messages to different clusters.

Is Kafka overkill? ›

As Kafka is designed to handle high volumes of data, it's overkill if you need to process only a small amount of messages per day (up to several thousand). Use traditional message queues such as RabbitMQ for relatively smaller data sets or as a dedicated task queue.

Is Kafka batch or stream? ›

As a technology that enables stream processing on a global scale, Kafka has emerged as the de facto standard for streaming architecture.

What are the 3 types of streams? ›

One method of classifying streams is through physical, hydrological, and biological characteristics. Using these features, streams can fall into one of three types: perennial, intermittent, and ephemeral.

What are the two types of stream? ›

There are two basic types of stream defined by Java, called byte stream and character stream.

What are the 4 major Kafka Apis? ›

The Admin API for inspecting and managing Kafka objects like topics and brokers. The Producer API for writing (publishing) to topics. The Consumer API for reading (subscribing to) topics. The Kafka Streams API to provide access for applications and microservices to higher-level stream processing functions.

Is Kafka stream immutable? ›

Just like a topic in Kafka, a stream in the Kafka Streams API consists of one or more stream partitions. A stream partition is an, ordered, replayable, and fault-tolerant sequence of immutable data records, where a data record is defined as a key-value pair.

Is KSQL and ksqlDB same? ›

For the purposes of this topic, "ksqlDB" refers to ksqlDB 0.6. 0 and beyond, and "KSQL" refers to all previous releases of KSQL (5.3 and lower). ksqlDB is not backward compatible with previous versions of KSQL. This means that, ksqlDB doesn't run over an existing KSQL deployment.

How many cores does Kafka need? ›

On-Premise
ComponentNodesCPU
Control Center18 cores or more
Kafka Broker3Dual 12 core sockets
Kafka Connect2Typically not CPU- bound. More cores is better than faster cores.
KSQL24 cores
3 more rows
15 Dec 2021

Is Kafka CPU or memory intensive? ›

Kafka Connect itself does not use much memory, but some connectors buffer data internally for efficiency. If you run multiple connectors that use buffering, you will want to increase the JVM heap size to 1GB or higher.

Is Kafka synchronous or asynchronous? ›

Kafka is a powerful stream processing tool, but it's an asynchronous tool.

Does uber use Kafka? ›

Uber has one of the largest deployments of Apache Kafka® in the world. It empowers a large number of real-time workflows at Uber, including pub-sub message buses for passing event data from the rider and driver apps, as well as financial transaction events between the backend services.

Is Kafka UDP or TCP? ›

Kafka uses a binary protocol over TCP. The protocol defines all APIs as request response message pairs.

Does Amazon use Kafka? ›

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.

Is Kafka FIFO or LIFO? ›

Kafka supports a publish-subscribe model that handles multiple message streams. These message streams are stored as a first-in-first-out (FIFO) queue in a fault-tolerant manner. Processes can read messages from streams at any time.

Why Kafka is so fast? ›

Why is Kafka fast? Kafka achieves low latency message delivery through Sequential I/O and Zero Copy Principle. The same techniques are commonly used in many other messaging/streaming platforms. Zero copy is a shortcut to save the multiple data copies between application context and kernel context.

Why Kafka is better than MQ? ›

Lastly, Apache Kafka makes it simpler to log events than other solutions because it does not erase messages after the receiving system reads them. With IBM MQ, a more conventional message queue system, any receiver can consume a message that an application pushes into the queue via push-based communication.

Is streams better than for loop? ›

Remember that loops use an imperative style and Streams a declarative style, so Streams are likely to be much easier to maintain. If you have a small list, loops perform better. If you have a huge list, a parallel stream will perform better.

What is the difference between stream () and Parallelstream ()? ›

A sequential stream is executed in a single thread running on one CPU core. The elements in the stream are processed sequentially in a single pass by the stream operations that are executed in the same thread. A parallel stream is executed by different threads, running on multiple CPU cores in a computer.

Why are streams better than for loops? ›

Streams provide scope for future efficiency gains.

Some people have benchmarked and found that single-threaded streams from in-memory List s or arrays can be slower than the equivalent loop. This is plausible because there are more objects and overheads in play.

How many queues can Kafka have? ›

You can bump up to a maximum of four instances in which case each consumer will be assigned to one partition.

When should I use a stream instead of a queue? ›

The basic difference is in the way they're used. In a stream, you usually only use one side of the operation: you open a stream to read, or write, but not both. Whereas with a queue, you're putting items on and taking them off.

Is Kafka Pub/Sub or queue? ›

In a very fast, reliable, persisted, fault-tolerance and zero downtime manner, Kafka offers a Pub-sub and queue-based messaging system. Moreover, producers send the message to a topic and the consumer can select any one of the message systems according to their wish.

Is ksqlDB a memory? ›

Each ksqlDB Server should have at least about 12 GB of memory.

Is ksqlDB a database? ›

ksqlDB is a database purpose-built for stream processing applications on top of Apache Kafka®.

Is ksqlDB ready for production? ›

KSQL is ready for production use, and integrated into CP

HAVING count(*) > 3; KSQL is fully integrated into the Confluent Platform.

What is spring boot binder? ›

A container object which Binds objects from one or more ConfigurationPropertySources .

What are the 4 major Kafka APIs? ›

The Admin API for inspecting and managing Kafka objects like topics and brokers. The Producer API for writing (publishing) to topics. The Consumer API for reading (subscribing to) topics. The Kafka Streams API to provide access for applications and microservices to higher-level stream processing functions.

What is Kafka in simple words? ›

Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.

How does Kafka store data? ›

Inside a Kafka broker, all stream data gets instantly written onto a persistent log on the filesystem, where it is cached before writing it to disk. The use of persistent storage, with its reliability, makes sense in the context of typical Kafka workloads.

What is the difference between a binder and a compression top? ›

Is The Compression Top a binder? We think of our Compression Top as a less restrictive alternative to a traditional binder. It's built from gentler fabrics than most binders currently on the market, and compresses more gently to protect you from breathing issues, excessive sweating, or skin irritation.

How do springback binders work? ›

Our Springback Binders allow you to bind your papers by just folding the front cover back on itself and inserting your pages, when the cover is closed the edges of the papers are gripped tightly in book format. At any time you may easily remove or insert more pages into these reusable binders.

What is a slide binder? ›

A: Slide binders are one of the easiest and economical ways of binding together loose sheets of paper.

What are 3 most common APIs? ›

Today, there are three categories of API protocols or architectures: REST, RPC and SOAP.

Can Kafka call a REST API? ›

REST APIs over Kafka using Zilla

This concept of a stream holds at both the network level for communication protocols and also at the application level for data processing. Zilla can be configured to map REST APIs to Kafka using the http-kafka binding in zilla.

Where is Kafka data stored? ›

Kafka stores all the messages with the same key into a single partition. Each new message in the partition gets an Id which is one more than the previous Id number. This Id number is also called the Offset.

Is Kafka a database? ›

Apache Kafka is not just a publisher/subscriber messaging system that sends data from one point to another. It's an event streaming platform. Associating databases with messaging solutions such as Kafka is a bit skewed. But Kafka has database-like features that make it primed to replace databases.

What language is Kafka written in? ›

Apache Kafka

Is Kafka a memory or disk? ›

Low-Latency IO:

Kafka relies on disk for storage and caching. but the problem is, disks are slower than RAM. It achieves low latency as random access memory(RAM) through Sequential IO.

Can Kafka store data forever? ›

>Kafka can perfectly keep your data around forever. In the sense that you can fiddle with it to the point where it doesn't purge things automatically, sure. But RDBMS provides more than the promise that it won't delete your data after a set period of time.

How many days Kafka can store data? ›

The Kafka cluster retains all published messages—whether or not they have been consumed—for a configurable period of time. For example if the log retention is set to two days, then for the two days after a message is published it is available for consumption, after which it will be discarded to free up space.

Videos

1. Kafka Streams using Spring Cloud Stream | Microservices Example | Tech Primers
(Tech Primers)
2. How To Use Spring Cloud Stream With Kakfa
(Refactor First)
3. Short demo of how to do joins in Kafka Streams
(Manning Publications)
4. How to Transform a Stream of Events Using Kafka Streams | Kafka Tutorials
(Confluent)
5. Kafka Streams: Zero to Hero - Ep 2 - Stateless Processing - Part 1
(Programming with Mati)
6. Crossing The Streams: Rethinking Stream Processing with KStreams and KSQL - Devnexus 2018
(Viktor Gamov)

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