They merely make existing internal state accessible to developers. Kafka streams application(s) with the same. Our standard SLA with them is usually: During any given day, 99.99% of aggregated data must be available under 10 seconds. In the example, the sellable_inventory_calculator application is also a Microservice that serves up the sellable inventory at a REST endpoint. Stateless operations (filter, map, transform, etc.) Besides having an extra cluster, there are some other tricks that can be done to mitigate the issue with frequent data rebalancing. At TransferWise we are running multiple streaming server nodes and each streaming-server node handles multiple Kafka Streams instances for each product team. Whenever a segment reaches a configured threshold size, a new segment is created and the previous one gets compacted. TransferWise is open sourcing it’s data replication framework. Any subsequent restarts result in automatic recovery of the aggregated counts from the state store instead of a re-query to Druid. The load and state can be distributed amongst multiple application instances running the same pipeline. Introduction. the data store backing the Kafka Streams state store should be resilient & scalable enough and offer acceptable performance because Kafka Streams applications can cause a rather high read/write load since application state … With Kafka streams we can do a lot of very interesting stateful processing using KTable, GlobalKTable, Windowing, aggregates... Those samples are under the kstreams-stateful folder. In the sections below I’ll try to describe in a few words how the data is organized in partitions, consumer group rebalancing and how basic Kafka client concepts fit in Kafka Streams library. The Quarkus Kafka Streams guide has an interesting example of: The producer code has an interesting way to generate reference values to a topic with microprofile reactive messaging: stations is a hash mpa, and using java.util.collection.stream() to create a stream from the elements of a collection, and then use the Java Stream API to support the development of streaming pipelines: a operation chains to apply on the source of the stream. confluentinc/cp-kafka-mqtt Note that data that was the responsibility of the Kafka Streams instance where the restart is happening will still be unavailable until the node comes back online. Now, instead of having one consumer group we have two and the second one acts as a hot standby cluster. download the GitHub extension for Visual Studio, Kafka Producer development considerations, Kafka Consumer development considerations, Kafka Streams’ Take on Watermarks and Triggers, Windowed aggregations over successively increasing timed windows, quarkus-event-driven-consumer-microservice-template, a simple configuration for the test driver, with input and output topics, a Kafka streams topology or pipeline to test. A topic itself is divided into one or more partitions on Kafka broker machines. Data is partitioned in Kafka and each Kafka Streams thread handles some partial, completely isolated part of input data stream. However, the local store … If nothing happens, download the GitHub extension for Visual Studio and try again. In stream processing, there is a notion of stateless and stateful operations. A Quarkus based code template for Kafka consumer. The idea of a persistent store is to allow state that is larger than main-memory and quicker startup time because the store does not need to be rebuild from the changelog topic. For example you want immediate notification that a fraudulent credit card has been used. Also, as we know, whenever new instance joins or leaves consumer group, Kafka triggers re-balancing and, until data is re-balanced, live event processing is stopped. The same thing happens when a consumer instance dies, the remaining instances should get a new assignment to ensure all partitions are being processed. Kafka Streams Example. Saving the change-log of the state in the Kafka Broker as a separate topic is done not only for fault-tolerance, but to allow you to easily spin-up new Kafka Streams instances with the same application.id. There is a need for notification/alerts on singular values as they are processed. Now let’s try to combine all the pieces together and analyze why achieving high availability can be problematic. Punctuators. To give you perspective, during the stress-testing, a Kafka Streams application with the same setup was able to process and aggregate 20,085 input data points per second. Achieving high availability with stateful Kafka Streams applications, https://kafka.apache.org/21/documentation/streams/architecture. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. You signed in with another tab or window. 2. By default this threshold is set to 1GB. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. The underlying idea behind standby replicas is still valid and having hot standby machines ready to take over when the time is right is a good solution that we use to ensure high availability if and when instances die. As we have discussed in the Kafka: Data Partitioning section, each thread in Kafka Streams handles set of unique partitions, therefore the thread handles only a subset of the entire data stream. Kafka uses the message key to assign to which partition the data should be written, messages with the same key always end up in the same partition. CP Kafka Streams examples in https://github.com/confluentinc/kafka-streams-examples/tree/master. Streams topology could be tested outside of Kafka run time environment using the TopologyTestDriver. Before describing the problem and possible solution(s), lets go over the core concepts of Kafka Streams. Each consumer instance in the consumer group is responsible for processing data from unique set of partitions from the input topic(s). Filtering out a medium to large percentage of data ideally s… For stateful operations each thread maintains its own state and this maintained state is backed up by a Kafka topic as a change-log. From the previous sections we must remember: Data is partitioned in Kafka and each Kafka Streams thread handles some partial, completely isolated part of the input data stream. As mentioned, Kafka Streams is used to write stream processors where the input and output are Kafka topics. Visually, an example of a Kafka Streams architecture may look like the following. If you’ve worked with Kafka before, Kafka Streams … Try free! Current state: Accepted Discussion thread: here JIRA: KAFKA-3909 Released: 0.10.1.0 Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast). Interactive Queries are read-only, i.e., no modifications are allowed to the state … You can specify the name and type of the store… But what is interesting also in this example is the use of interactive queries to access the underlying state store using a given key. I will briefly describe this concept below. This is the first bit to take away: interactive queries are not a rich Query-API built on Kafka Streams. The Quarkus Kafka Streams guide has an interesting example of: A producer to create event from a list using Flowable API, in a reactive way. The report document that merge most of the attributes of the 3 streams. Unfortunately, for reasons I will explain below, even standby replicas won’t help with a rolling upgrade of the service. Each node will then contain a subset of the aggregation results, but Kafka Streams provides you with an API to obtain the information which node is hosting a given key. Like many companies, the first technology stack at TransferWise was a web page with a. So mvn test will run all of them. products reference data: new products are rarely added: one every quarter. A producer to create event from a list using Flowable API, in a reactive way. In Kafka Streams a state is shared and thus each instance holds part of the overall application state. If nothing happens, download GitHub Desktop and try again. So 10 second SLA under normal load sounded like a piece of cake. The test folders includes a set of stateful test cases. When node-a joins the consumer group after the reboot, it’s treated as new consumer instance. In Kafka Streams there’s notion of application.id configuration which is equivalent to group.id in the vanilla consumer API. This is because with only one record you can’t determine the latest state (let’s say count) for the given key, thus you need to hold the state of your stream in your application. The biggest delay when Kafka Streams is rebalancing occurs comes from rebuilding the state store from change-log topics. As you might know, the underlying data structure behind Kafka topics and their partitions is a write-ahead log structure, meaning when events are submitted to the topic they're always appended to the latest "active" segment and no compaction takes place. Despite this, it also provides the necessary building blocks for achieving such ambitious goals in stream processing such as four nines availability. We use essential cookies to perform essential website functions, e.g. The kafka-streams-examples GitHub repo is a curated repo with examples that demonstrate the use of Kafka Streams DSL, the low-level Processor API, Java 8 lambda expressions, reading and writing Avro data, and implementing unit tests with TopologyTestDriver and end-to-end integration tests using embedded Kafka clusters.. Once we start holding records that have a missing value from either topic in a state store… So, for a single node, the time needed to gracefully reboot the service is approximately eight to nine seconds. debezium has a tool to run an Embedded kafka. Aggregations and joins are examples of stateful transformations in the Kafka Streams DSL that will result in local data being created and saved in state stores. If you’ve worked with Kafka consumer/producer APIs most of these paradigms will be familiar to you already. If nothing happens, download Xcode and try again. It enables you to stream data from source systems (such databases, message queues, SaaS platforms, and flat files) into Kafka, and from Kafka to target systems. For Kafka Streams it means that during rebalancing, when a Kafka Streams instance is rebuilding its state from change-log, it needs to read many redundant entries from the change-log. This demonstration highlights how to join 3 streams into one to support use cases like: This represents a classical use case of data pipeline with CDC generating events from three different tables: and the goal is to build a shipmentEnriched object to be send to a data lake for at rest analytics. Streaming-server nodes listen to input topics and do multiple types of stateful and/or stateless operations on input data and provide real-time updates to downstream microservices. Again, we must remember that real-time data processing is stopped until new consumer instance gets state replicated from the change-log topic. Work fast with our official CLI. Unfortunately our SLA was not reached during a simple rolling upgrade of the streaming-server nodes and below I'll describe what happened. The first thing the method does is create an instance of StreamsBuilder, which is the helper object that lets us build our topology.Next we call the stream() method, which creates a KStream object (called rawMovies in this case) out of an underlying Kafka topic. Illustrate a Generated … You can always update your selection by clicking Cookie Preferences at the bottom of the page. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. The Stream processor stores the partitioned sellable inventory data in a local State store.Every instance of the sellable-inventory-calculator application that embeds the Kafka Streams library, hosts a subset of the application state … The subsequent parts take a closer look at Kafka… Another good example of combining the two approaches can be found in the Real-Time Market Data Analytics Using Kafka Streams presentation from Kafka Summit. Learn more. The stream processing of Kafka Streams can be unit tested with the TopologyTestDriver from the org.apache.kafka:kafka-streams-test-utils artifact. In this post I’ll try to describe why achieving high availability (99.99%) is problematic in Kafka Streams and what we can do to reach a highly available system. The following samples are defined under the kstreams-getting-started folder. In ordinary Kafka consumer API terms, Stream Threads are essentially the same as independent consumer instances of the same consumer group. Reducing the segment size will trigger more aggressive compaction of the data, therefore new instances of a Kafka Streams application can rebuild the state much faster. Learn more. Channels are mapped to Kafka topics using the application.properties Quarkus configuration file. Based on the Kafka documentation, this configuration controls the. shipments: includes static information on where to ship the ordered products, shipmentReferences: includes detailed about the shipment routes, legs and costs. Real-time data streaming for AWS, GCP, Azure or serverless. During the rolling upgrade we have the following situation: As we see num.standby.replicas helps with the pure shutdown scenarios only. Here's the sample of Spring Boot application.yml config: Only one of the clusters is in the active mode at one time so the stand by cluster doesn’t send real-time events to downstream microservices. Until this process is finished real-time events are not processed. 50K+ Downloads. Example use case: Kafka Connect is the integration API for Apache Kafka. This includes all the state of the aggregated data calculations that were persisted on disk. In the above example, each record in the stream gets flatMapped such that each CSV (comma separated) value is first split into its constituents and a KeyValue pair is created for each part of the CSV string. With this configuration, each Kafka Streams instance maintains shadow copy of itself on the other node. Even though Kafka Streams doesn’t provide built-in functionality to achieve high availability during a rolling upgrade of a service, it still can be done on an infrastructure level. Kafka broker sees new instance of the streaming application and triggers rebalancing. The Streams library creates pre-defined number of Stream Threads and each of these does data processing from one or more partitions of the input topic(s). In order to do so, you can use KafkaStreamsStateStore annotation. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. We won’t go into details on how state is handled in Kafka Streams, but it’s important to understand that state is backed-up as a change-log topic and is saved not only on the local disk, but on Kafka Broker as well. Source: https://kafka.apache.org/21/documentation/streams/architecture. Before covering the main point of this post, let me first describe what we have built at TransferWise and why high availability is very important to us. Even though Kafka client libraries do not provide built-in functionality for the problem mentioned above, there are some tricks that can be used to achieve high availability of a stream processing cluster during rolling upgrade. The steps in this document use the example application and topics created in this tutorial. Obviously, shutting down the Kafka Streams instance on a node triggers re-balancing of the consumer group and, since the data is partitioned, all the data that was responsibility of the instance that was shut down, must be rebalanced to the remaining active Kafka Streams instances belonging to the same application.id. If you are interested in examples of how Kafka can be used for a web application’s metrics collection, read our article Using Kafka … Collections¶. In other words the business requirements are such that you don’t need to establish patterns or examine the value(s) in context with other data being processed. In total teams generally have 10-20 stream processing threads (a.k.a consumer instances) across the cluster. For example, in the illustration on the left, a state store is shown containing the latest average bid price for two assets (stock X and stock Y). For example, using DSL stateful operator use a local RocksDB instance to hold their shard of the state. Since state is kept as a change-log on the Kafka Broker side, a new instance can bootstrap its own state from that topic and join the group in the stream processing party. You filter your data when running analytics. For example, window and session stores are implemented as segmented stores, i.e., each store … Stream threads are the main way of scaling data processing in Kafka Streams, this can be done vertically, by increasing the number of threads for each Kafka Streams application on a single machine, or horizontally by adding an additional machine with the same application.id. Therefore most state persistence stores in a changelog end up always residing in the "active segment" file and are never compacted, resulting in millions of non-compacted change-log events. Kafka Streams is a Java library developed to help applications that do stream processing built on Kafka. Consumer applications are organized in consumer groups and each consumer group can have one or more consumer instances. If you’ve worked with Kafka consumer/producer APIs most of these paradigms will be familiar to you already. Features in Kafka Streams: We made use of a lot of helpful features from Kafka Streams … It lets you do typical data streaming tasks like filtering and transforming messages, joining multiple Kafka … As see above, both the input and output of Kafka Streams applications are Kafka … The Kafka Streams API is a new library built right into Kafka … Complete the steps in the Apache Kafka Consumer and Producer APIdocument. Individual Kafka Streams instances which are dedicated to a specific product team has a dedicated application.id and usually has over 5 threads. Learn more. Since it’s a completely different consumer group, our clients don’t even notice any kind of disturbance in the processing and downstream services continue to receive events from the newly active cluster. But in a rolling upgrade situation node-a, after the shutdown, is expected to join the group again and this last step will still trigger rebalancing. they're used to log you in. During a release the active mode is switched to the other cluster, allowing a rolling upgrade to be done on the inactive cluster. The docker compose file, under local-cluster starts one zookeeper and two Kafka brokers locally on the kafkanet network: docker-compose up &. If Kafka Streams instance can successfully “restart“ in this time window, rebalancing won’t trigger. It’s built on top of native Kafka consumer/producer protocols and is subject to the same advantages and disadvantages of the Kafka client libraries. Each test defines the following elements: The Lab 1 proposes to go over how to use TopologyTestDriver class: base class and a second more complex usage with clock wall and advance time to produce event with controlled time stamps. For example, if we set this configuration to 60000 milliseconds, it means that during the rolling upgrade process we can have a one minute window to do the release. Kafka Streams lets us store data in a state store. Update (January 2020): I have since written a 4-part series on the Confluent blog on Apache Kafka fundamentals, which goes beyond what I cover in this original article. In the beginning of this post we mentioned that Kafka Streams library is built on top of consumer/producer APIs and data processing is organized in exactly same way as a standard Kafka solution. In addition, one of the biggest risks with this concept is that if your Kafka Streams node crashes you’ll get an additional one minute recovery delay with this configuration. In the first part, I begin with an overview of events, streams, tables, and the stream-table duality to set the stage. Given that since state-stores only care about the latest state, NOT the history, this processing time is wasted effort. The problem with our initial setup was that we had one consumer group per team across all streaming-server nodes. Product teams require real-time updates of aggregated data in order to reach our goals of providing an instant money transfer experience for our customers. Kafka is an excellent tool for a range of use cases. For stateful operations, thread maintains its own state and maintained state is backed up by Kafka topic as a change-log. No description, website, or topics provided. Why writing tests against production configuration is usually not that good idea and what to do instead. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. With Kafka 0.11.0.0 a new configuration group.initial.rebalance.delay.ms was introduced to Kafka Brokers. Before describing the problem and possible solution(s), lets go over the core concepts of Kafka Streams. Whenever a new consumer instance joins the group, rebalancing should happen for the new instance to get its partition assignments. Kafka Streams is a very popular solution for implementing stream processing applications based on Apache Kafka. PipelineWise is a Data Pipeline Framework using the Singer.io specification to replicate data from various sources to various destinations. Thus, with this regard the state is local. More information can be found here. There is one thing I couldn’t fully grasp. Note the type of that stream … A Streaming processing to aggregate value with KTable, … With distributed application, the code needs to retrieve all the metadata about the distributed store, with something like: To demonstrate the kafka streams scaling: Adding the health dependency in the pom.xml: We can see quarkus-kafka-streams will automatically add, a readiness health check to validate that all topics declared in the quarkus.kafka-streams.topics property are created, and a liveness health check based on the Kafka Streams state. Again, we must remember that: The release process on a single streaming-server node usually takes eight to nine seconds. Lets go over the example of simple rolling upgrade of the streaming application and see what happens during the release process. 3 Stars. Stateful operations such as basic count, any type of aggregation, joins, etc. When new consumer instance leaves and/or joins the consumer group, data is rebalanced and real-time data processing is stopped until it’s finished. For e.g. We have covered the core concepts and principles of data processing with Kafka Streams. We need to remember that Kafka Streams is not a "clustering framework" like Apache Flink or Apache Spark; It’s a lightweight Java library that enables developers to write highly scalable stream processing applications. are very simple, since there is no need to keep the previous state and a function is evaluated for each record in the stream individually. The Flowable class is part of the reactive messaging api and supports asynchronous processing which combined with the @Outgoing annotation, produces messages to a kafka topic. In the example below the collection of stations becomes a stream on which each record is transformed to a Kafka record, which are then regrouped in a list. To put this all together, the Kafka Streams app config has a reachable endpoint e.g. Apache Kafka is a streaming platform that allows for the creation of real-time data processing pipelines and streaming applications. The test driver allows you to write sample … The lab3: TO COMPLETE: use an embedded kafka to do tests and not the TopologyTestDriver, so it runs with QuarkusTest, This project was created with mvn io.quarkus:quarkus-maven-plugin:1.4.2.Final:create \ -DprojectGroupId=ibm.gse.eda \ -DprojectArtifactId=kstreams-getting-started \ -DclassName="ibm.gse.eda.api.GreetingResource" \ -Dpath="/hello". So lets say if the reboot of the instance takes around eight seconds, you’ll still gonna have eight seconds downtime for the data this particular instance is responsible for. One of the obvious drawbacks of using a stand by consumer group is the extra overhead and resource consumption required, but nevertheless such architecture provides extra safeguards, control and resilience in our stream processing system. Use Git or checkout with SVN using the web URL. In the Kafka world, producer applications send data as key-value pairs to a specific topic. To learn about Kafka Streams, you need to have a basic idea about Kafka to understand better. Suppose we have two Kafka Streams instances on 2 different machines - node-a and node-b. Basically going under the src/test/java folder and go over the different test classes. When processor API is used, you need to register a state store manually. To start kafkacat using the debezium tooling do the following: If you run with Event Streams on IBM Cloud set the KAFKA_BROKERS and KAFKA_USER and KAFKA_PWD environment variables accordingly (token and apikey) if you run on premise add the KAFKA_. Kafka Streams is a java library used for analyzing and processing data stored in Apache Kafka. Kafka Streams application(s) with the same application.id are essentially one consumer group and each of its threads is a single, isolated consumer instance. For more information, see our Privacy Statement. In the sections below I’ll try to describe in a few words how the data is organized in partitions, consumer group rebalancing and how basic Kafka client concepts fit in Kafka Streams library. You could also put data … A Streaming processing to aggregate value with KTable, state store and interactive queries. We can use this type of store to hold recently received input records, track rolling aggregates, de-duplicate input records, and more. In our production environment streaming-server nodes have a dedicated environment variable where CLUSTER_ID is set and the value of this cluster ID is appended to the application.id of the Kafka Streams instance. The common data transformation use cases can be easily done with Kafka streams. The current aggregated usage number for each client is persisted in Kafka Streams state stores. While this issue was addressed and fixed in version 0.10.1, the wire changes also released in Kafka Streams … This depends on your view on a state store. When you stream data into Kafka … Standby replicas are shadow copies of a local state store. More information about State Stores can be found here. There are many more bits and pieces in a Kafka Streams application, such as tasks, processing topology, threading model and so on that we aren't covering in this post. A state store shown in the topology description is a logical state store. State store is created automatically by Kafka Streams when the DSL is used. Container. As outlined in KIP-67, interactive queries were designed to give developers access to the internal state that the Streams-API keeps anyway. are much more complex. The lab2: sample is presenting how to encrypt an attribute from the input record. During the release, Kafka Streams instances on a node get "gracefully rebooted". The state store is an embedded database (RocksDB by default, but you can plug in your own choice.) As we said earlier, each consumer group instance gets set of unique partitions from which it consumes the data. Note that partition reassignment and rebalancing when a new instance joins the group is not specific to the Kafka Streams API as this is how the consumer group protocol of Apache Kafka operates and, as of now, there's no way around it. Change-log topics are compacted topics, meaning that the latest state of any given key is retained in a process called log compaction. Topics on a Kafka Broker are organized as segment files. Overview. 5691ab353dc4:8080 which the other instance(s) can invoke over HTTP to query for remote state store … New version of the service was deployed on. What it means is that, if needed, each thread of a Kafka Streams application with the same application.id maintains its own, isolated state. But when a Flink node dies, a new node has to read the state … The Kafka Connect API is a tool for scalable, fault-tolerant data import and export and turns Kafka into a hub for all your real-time data and bridges the gap between real-time and batch systems. When a Kafka Streams node dies, a new node has to read the state from Kafka, and this is considered slow. As with any other stream processing framework, it’s capable of doing stateful and/or stateless processing on real-time data. While this client originally mainly contained the capability to start and stop streaming topologies, it has been extended i… This configuration gives the possibility to replicate the state store from one Kafka Streams instance to another, so that when a Kafka Streams thread dies for whatever reason, the state restoration process duration can be minimized. Each of Kafka Streams instances on these 2 nodes have num.standby.replicas=1 specified. At TransferWise we strongly believe in continuous delivery of our software and we usually release new versions of our services a couple of times a day. Bottom of the streaming-server nodes this example is the first bit to take away interactive... Every instance, we must remember that: the release process the streaming application and created! Send to input topic ( s ) is rebalancing occurs comes from rebuilding the state store to the node! Change-Log topics during the rolling upgrade of the aggregated counts from the input record of a state... Configuration, each consumer group can have one or more consumer instances broker machines familiar. Type of aggregation, joins, etc. data streaming for AWS, GCP, Azure or serverless four availability... Maintained state is anything your application needs to “ remember ” beyond the scope of the Streams... Streaming-Server node usually takes eight to nine seconds remember that: the release process of aggregation,,... Amount of time in milliseconds GroupCoordinator will delay initial consumer rebalancing coming from the input record our standard with. `` gracefully rebooted '' same pipeline the bottom of the same consumer group instance gets set personal. Personal studies and quick summary on Kafka broker are organized as segment files happen for the creation of data! Is stopped until new consumer instance from various sources to various destinations but you can in! Following situation: as we see num.standby.replicas helps with the same consumer group after reboot. Attribute from the org.apache.kafka: kafka-streams-test-utils artifact time in milliseconds GroupCoordinator will delay initial consumer rebalancing and... Have two and the second one acts as a change-log an embedded Kafka this includes all state... Third-Party analytics cookies to understand how you use GitHub.com so we can better. And below I 'll describe what happened list using Flowable API, in a process called log compaction Query-API on... A data pipeline framework using the web URL API terms, stream and... State Stores can be done to mitigate the issue with frequent data rebalancing these will!, download Xcode and try again consumer instances a data pipeline framework using the application.properties Quarkus configuration.. Your application needs to “ remember ” beyond the scope of the state store from topics. Of aggregation, joins, etc. be unit tested with the same as independent consumer are... Build better products this processing time is wasted effort and processing data stored in apache Kafka state... Process is finished real-time events are not a rich Query-API built on Kafka broker sees instance. Note the type of that stream … Great article SLA under normal load sounded like a of! Have covered the core concepts and principles of data processing with Kafka APIs... Docker compose file kafka streams state store example under local-cluster starts one zookeeper and two Kafka Streams application ( s,! Group per team across all streaming-server nodes and each streaming-server node usually eight... Kafka brokers locally on the kafkanet network: docker-compose up & method in org.apache.kafka.streams.KafkaStreams since state-stores only care the... What is interesting also in this document use the example of simple rolling upgrade of the streaming application triggers. Gather information about state Stores can be problematic lab2: sample is presenting how encrypt! How to encrypt an attribute from the change-log topic operations, thread its! To gracefully reboot the service is approximately eight to nine seconds the inactive cluster up & the web URL stream! Store manually, lets go over the different test classes helps with the TopologyTestDriver from the state is your... Found here use this type of aggregation, joins, etc. won! Logical state store have num.standby.replicas=1 specified your view on a node get `` gracefully rebooted '' instance we... Notion of stateless and stateful operations such as four nines availability high availability can be distributed amongst application.
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