kafka streams query state store

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Ensure store operations are thread-safe. The problem this document addresses is that this state is hidden from application developers and they cannot access it directly. 5 wordCounts.to(Serdes.String(), Serdes.Long(), "streams-wordcount-output"); In line 4, the aggregation already maintains state in a store called StoreName, however that store cannot be directly queried by the developer. Conceptually the code to access such a store would look like this: KafkaStreams streams = new KafkaStreams(..); ReadOnlyKeyValueStore store = streams.store("storeName", QueryableStoreTypes.keyValueStore()); The state store is discovered by querying the KafkaStreams instance. We propose that they keep track of this information by piggybacking on the consumer group membership protocol. Sign in to view. This means a (Java) application is needed which starts and runs the streaming pipeline, reading from and writing to the Apache Kafka cluster. Explicitly require names for all created KTables (thus their changelog state stores), Materialize all KTables (today some KTables are materialized and some are not). that users provide to listen for queries (e.g., using REST APIs). The stream store namespace is local to a KStreams instance, i.e., it is part of the same process that the KStreams instance is in. Hence, the user might not see the latest value for their data. In addition to storing the state, Kafka Streams has built-in mechanisms for fault-tolerance of these state stores. in a round-robin scheme), and the REST endpoints will return the results, internally forwarding the request to the correct processor if necessary. The supplied host:port pair will form part of the StreamsMetadata returned from the the above mentioned API calls. The individual KafkaStreams instances will know about all mappings between state store names, keys and KafkaStream instances. The stream topology is defined in App.java: The above definition of our KTable will allow us to retrieve exactly one value per customer id (key) later on. As we will see in the next step, it is sometimes necessary to redirect incoming queries among the stream processor instances. Implementations of this interface for the StateStores that are part of the KafkaStreams library will be provided by this KIP. A single StreamsMetadata object represents the state of a Kafka Streams instance. Today a Kafka Streams application will implicitly create state. Kafka Streams includes state stores that applications can use to store and query data. Another limitation is that it might not be possible to run this on a single machine. Kafka Streams tasks and state stores Showing 1-7 of 7 messages. On catching this exception the user can try again. Every stream task in a Kafka Streams application … The DSL allows users to make a copy of the data (using the through operator) but this leads to a doubling in the amount of state that is kept. The clients can then send requests to any of the stream processors (e.g. returned from the the above mentioned API calls. To bootstrap the discovery, a user can simply query the discovery instance of any one of the KafkaStream instances she operates, i.e., bootstrap happens within a single KafkaStream instance. We propose to make the Kafka Streams internal state stores visible to external queries. by querying the KafkaStreams instance. Otherwise, we can obtain it from the local store, or return a HTTP 404 response if the key does not exist. For each stream processor application instance, at least one CPU core should be reserved. Subsequently, the developer might instantiate its own database after reading the data from that topic (this step is not shown above). Because the customer id is chosen as the key for each message, data belonging to a given customer will always be inserted into the same partition of the topic. To illustrate the architecture of a Kafka Streams application that employs state stores, imagine the following scenario: As a railway operator, every time a customer books a trip on our website, a new message consisting of the customer id and a timestamp is inserted into a Kafka topic. As most of our customers deploy their Kafka Streams applications on Kubernetes, a Kubernetes Service using the integrated LoadBalancer service type is also a good fit for this use case: With a central reverse proxy in place, we can make a more educated decision about which stream processor to consult for a given request. Hence, as part of this proposal we add the need to expose all relevant state store names to the user. If the StateStore is not valid the operation needs to return an exception InvalidStateStore. The default StreamPartitioner will be used for key partitioning. Closed dguy wants to merge 30 commits into apache: trunk from ... {@link org.apache.kafka.streams.KafkaStreams#getStore(String, QueryableStoreType)} * To access and query the {@link StateStore}s that are port of a Topology: This comment has been minimized. In particular, the latest records for a topic might be residing on a remote StateStore (that acts like a cache for that topic). is local to a KStreams instance, i.e., it is part of the same process that the KStreams instance is in. For each input partition, Kafka Streams creates a separate state store, which in turn only holds the data of the customers belonging to that partition. The load on an individual stream processor depends on the amount of data and queries it has to handle. component. 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. First, expose all state store names to the DSL. Using the Kafka Streams DSL, which is inspired by the Java Stream API, stream processors, and state stores can be flexibly chained. KAFKA-3912: Query local state stores #1565. By exposing a simple REST endpoint which queries the state store, the latest aggregation result can be … Discovery with this option is implicitly done by the consumer that reads from the topic. This could happen after a failure of a server on which KafkaStreams is running, or for load balancing. One potential drawback of this method is lack of consistency. is calling methods of the StateStore object. Range queries on a windowed state store include a time window and a key range. If you’re plugging in a custom state store, then you’re on your own for state management (though you might want to read along anyway as many of the same concepts apply!). Instead, the developer makes a copy of the data in that store into a topic called. Contributor typo: port->part. Create an instance of T (usually a facade) that developers can use to query the underlying StateStores . This is where Kafka Streams interactive queries shine: they let you directly query the underlying state store of the pipeline for the value associated to a given key. Evaluate Confluence today. Nevertheless, being able to query new data at this early stage in the pipeline would avoid the delays of traditional processing pipelines that usually include long-running batch-preprocessing steps and would give end-users almost instant access to incoming data. Always enforce KTable materialization upon creation (i.e. The application can then implement its own query patterns on the data and route the requests among instances as needed. Instead, the developer makes a copy of the data in that store into a topic called streams-wordcount-output. This KTable is materialized as an in-memory state store, which facilitates fast lookups. The figure below shows two KStream instances on potentially different servers and two state stores each backed up by their own Kafka topic. Subsequently, the developer might instantiate its own database after reading the data from that topic (this step is not shown above). Although the complexity of queries are limited, for example in comparison to KSQL, the proposed architecture can still provide a lightweight and easily scalable foundation for many types of applications. Using an event-streaming approach, we can materialize the data locally via the Kafka Streams API. An additional proposed component in the diagram is a logical discovery component. Unlike other streaming query engines that run on specific processing clusters, Kafka Streams is a client library. one stream worker thread updating the store, and one user thread querying the store. Conceptually the code to access such a store would look like this: ReadOnlyKeyValueStore store = streams.store("storeName", QueryableStoreTypes. operator) but this leads to a doubling in the amount of state that is kept. Avoid duplicating data 2. Powered by a free Atlassian Confluence Open Source Project License granted to Apache Software Foundation. Hence, the discovery API is part of the KafkaStreams instance. Ensure store operations are thread-safe. This is the first bit to take away: interactive queries are not a rich Query-API built on Kafka Streams. An additional proposed component in the diagram is a logical. While this client originally mainly contained the capability to start and stop streaming topologies, it has been extended in Kafka 0.… The. Now, in order to make this information accessible from outside of the Kafka Streams processors, we need to expose a service endpoint on each of the stream processor application instances and answer incoming requests from the internal state store that is managed by Kafka Streams. It already has methods to obtain host/port of the instance, stores/topic partitions it hosts as an active. it might not be possible to run this on a single machine. I.e., a single state store could have concurrent access, e.g. Implementations of this interface for the StateStores that are part of the KafkaStreams library will be provided by this KIP. The figure below shows two KStream instances on potentially different servers and two state stores each backed up by their own Kafka topic. We must also document the valid characters for a state store name. If the StateStore is not valid the operation needs to return an exception. It might be hard to provision the downstream machine correctly. Developers using the. On multicore systems, it is possible to increase the number of stream threads per application instance, which mitigates overhead incurred by starting a separate Java application per CPU core. These Key-Value stores can be continuously filled with new messages from a Kafka topic by defining an appropriate stream processor, so that it is now possible to quickly retrieve messages from the underlying topic. Now, the proxy can look up the correct stream processor by using the information provided by our new REST endpoint. In particular, we propose to add the above mapping Map> to StreamPartitionAssignor so that each consumer knows about all the other tasks metadata and host states in the system. To avoid having to implement the round-robin selection of stream processors in every consumer application, it is often useful to install a reverse proxy, such as nginx or haproxy. The Stream processor stores the partitioned sellable inventory data in a local State store. With the architecture presented so far, we have nine state stores that can be used to retrieve the latest booking date of the customers belonging to the respective input topic partition. General approaches for obtaining this execution information include the following: 1. Furthermore, if you have N applications that need to query the state, you need to duplicate the state N times. The data flow so far . It is a great messaging system, but saying it is a database is a gross overstatement. DNS names or IP addresses with ports). It is not clear what it would mean to update a state store from outside the stream processing framework. This walkthrough uses the Java API for Kafka Streams. Whenever operations are made against this object it must first check if the underlying StateStore is valid. Once a state store is found, then the store is queried using the APIs in step 1. The integration tests use an embedded Kafka clusters, feed input data to them (using the standard Kafka producer client), process the data using Kafka Streams, and finally read and verify the output results (using the standard Kafka consumer client). It is assumed that the user will write such a listener themselves. However, this KIP stops short of providing a listener on each KafkaStream instance that actually listens for remote queries (e.g., through REST). The DSL allows users to make a copy of the data (using the. Kafka Streams is built as a library that can be embedded into a self-contained Java or Scala application. Logging from the application and querying those logs by other applications 2. Finally, we can define our REST API, which will perform the fast lookups provided by the state stores accessible from outside the stream processors. The namespace would be global in this case, but instead of worrying about the StateStore namespace, we would be interested in the topic names instead (each state store is often backed into a Kafka topic). If a state store would write into the state directory directly, it might conflict with others state stores and thus, data might get corrupted and/or Streams might fail with an error. I recommend my clients not use Kafka Streams because it lacks checkpointing. If any of your Kafka Streams app instance fails, another one can come up, restore the current state from Kafka and continue processing. The supplied host:port pair will form part of the. One important feature of Kafka Streams are state stores, offering an abstraction of a fast local Key-Value Store that can be read and written to when processing messages with Kafka Streams. Bootstrapping. This is shown in illustration (a): We propose to make the Kafka Streams internal state stores visible to external queries. Conceptually this component provides a lookup API that keeps track of the mapping between a state store name and the KafkaStreams instance that owns that state store. By making Kafka Streams internal lookup table available to the proxy, the additional hop from one stream processor to another can be avoided most of the times. Explicitly require names for all operations that create a state store, like, We propose adding an additional method to the, The QueryableStoreType interface, below, can be used to ‘plug-in’ different StateStore implementations to Queryable State. Query and Analysis of Existing Events: No need for another data store/data lake; ksqlDB (position first, but know the various limitations); Kafka-native analytics tool (e.g. For example, the Kafka Streams DSL automatically creates and manages such state stores when you are calling stateful operators such as join() or aggregate(), or when you are windowing a stream. Furthermore, Kafka Streams relies on using the store name as store directory name to perform internal cleanup tasks. Once a state store is found, then the store is queried using the APIs in step 1. Consequently, each service endpoint is responsible to redirect the query to the correct application instance if the customer data is not locally available. This is shown in illustration (a): Step 1 in proposal: expose state store names to DSL and local queries. A class that provides implementations of the QueryableStoreTypes that are part of KafkaStreams,i.e., Two new interfaces to restrict StateStore access to Read Only (note this only applies to implementations that are part of Kafka Streams). Ensure failure and rebalancing is handled correctly. wordCounts.to(Serdes.String(), Serdes.Long(), "streams-wordcount-output"); In line 4, the aggregation already maintains state in a store called, , however that store cannot be directly queried by the developer. Fewer moving pieces in the end-to-end architecture by avoiding unneeded state stores 4. On the other hand, persistent state stores can be restored faster in case a Kafka Streams application has failed and needs to restart. More on the user-defined agent below. If the rebalance has finished and the StateStores are open, subsequent operations will be successful. Any subsequent restarts result in automatic recovery of the aggregated counts from the state store instead of a re-query to Druid. This is a host:port pair supplied by the streams developer and should map to a Server running in the same instance of the KafkaStreams application. KSQL sits on top of Kafka Streams and so it inherits all of these problems and then some more. For the second part, an implementation outline follows: Implement read-only interface and QueryableStoreType for key value state store, Implement read-only interface and QueryableStoreType for windowed state store. To query the local ReadOnlyKeyValueStore it must be obtained via KafkaStreams#store ... (application state is shared over all running Kafka Streams instances) For non-local keys, a custom RPC mechanism must be implemented using KafkaStreams.allMetadata() to query the value of the key on a parallel running instance of your Kafka Streams application. Sticking to the example use case from above, we will assume that the customer ids and timestamps have a string format for simplicity, so both the keys and values of our Kafka messages can be (de-)serialized into Java Strings. In addition to storing the state, Kafka Streams has a built-in mechanism for fault-tolerance of these state stores. If the plugged in state store is a RocksDB key-value state store: If the plugged in state store is a RocksDB-backed window store: WindowStoreIterator fetch(K key, long timeFrom, long timeTo); The implication is that the user’s code will not have a direct reference to the underlying StateStores, but rather an Object that knows how to locate and query them. one stream worker thread updating the store, and one user thread querying the store. The API will provide four methods: Collection KafkaStreams.allMetadataForStore(String /* storeName */) would return only the StreamsMetadata that include the given store name. The internal state stores include all processor node stores used implicitly (through DSL) or explicitly (through low level Processor API). This functionality is referred to as interactive queries in Kafka Streams, and the following code examples are inspired by Confluent’s example applications. A user would create a RocksDb StateStore and instruct it to cache data from the desired topic. Moreover, the data volume per store is not limited by the amount of main memory when using persistent state stores. In other words, the DefaultPartitioner used in our Kafka producers needs to be re-implemented in the reverse proxy. Kafka Streams provides a builtin infrastructure for sharing this information automatically. Below are example implementations for KeyValueStore and WindowStore. The state is also used to store KTable’s data when they are materialized. Furthermore, Kafka Streams relies on using the store name as store directory name to perform internal cleanup tasks. The only purpose is to demonstrate the different approaches to the query side, one with Kafka Streams and a Local storage and the second with a dedicated Cassandra instance servicing the Query. To obtain a scalable application, we need to ensure that the processing load is equally balanced over all instances of the Kafka Streams application. Note: The TODO 1 - Add state store and TODO - 2: Add processor code later comments are placeholders for code that we will add in the upcoming sections. In order to achieve the maximum degree of parallel processing, we can start up to nine instances of the Kafka Streams application. Every task in Kafka Streams uses one or more state stores which can be accessed via APIs to store and query data required for processing. To bootstrap the discovery, a user can simply query the discovery instance of any one of the KafkaStream instances she operates, i.e., bootstrap happens within a single KafkaStream instance. Ability to colocate data and processing (e.g., in situations where many rows are scanned per operation). The operations of the state store will be the query-only operations of whatever state store the user has plugged in. In particular, the latest records for a topic might be residing on a remote StateStore (that acts like a cache for that topic). Complete the steps in the Apache Kafka Consumer and Producer APIdocument. This design option simply provides a discovery API and the ability to find a state store. Ability to colocate data and processing (e.g., in situations where many rows are scanned per operation). When using this object to build our REST service, the architecture looks as follows: Summarizing, our proposed architecture makes use of Kafka topics to reliably store message data at rest and maintains a second representation of the data in state stores to support fast queries. Specifically, one or more Kafka producers insert these messages into a topic with nine partitions. The steps in this document use the example application and topics created in this tutorial. In addition, this leads to extra IOs to external databases/key value stores that could potentially slow down the entire pipeline. Thus, we have to make sure that the stream processors know the REST endpoint addresses of each other. This is a host:port pair supplied by the streams developer and should map to a Server running in the same instance of the KafkaStreams application. Today, some state store names are hidden from the developer and are internal to Kafka Streams. The query method is calling methods of the StateStore object. Querying the local stores on an instance will only return data locally available on that particular instance. It is assumed that the user will write such a listener themselves. A large keyspace could result in too much data to fit in memory and/or on local disk. Benefits of this approach include: 1. This state is used for storing intermediate data such as aggregation results. I created a Kafka Streams app … where external processes put a command message on an “input.cmd” topic with a pending status, the app process it and put an updated command message on the same “input.cmd” topic with a new status indicating if it was processed with or without errors. Building on top of this Kafka Streams functionality, we create a unified REST API that provides a single querying endpoint for a given Kafka topic. An additional configuration parameter, StreamsConfig.APPLICATION_SERVER_CONFIG, will be added. In summary, combining Kafka Streams processors with State Stores and an HTTP server can effectively turn any Kafka topic into a fast read-only key-value store. This makes it difficult for a developer to query them. The individual KafkaStreams instances will know about all mappings between state store names, keys and KafkaStream instances. You may also want to provide an interface to restrict operations to read-only and a Composite type for providing a faćade over the potentially many instances of the underlying store (see example CompositeReadOnlyKeyValueStore below). Conceptually this component provides a lookup API that keeps track of the mapping between a state store name and the KafkaStreams instance that owns that state store. Collection KafkaStreams.allMetadata()), has fields such as list of assigned partitions, list of state store names and, that includes hostname / port, etc. Operations on in-memory state stores are even faster compared to the persistent variant, which internally uses a RocksDB store. KIP-67: Queryable state for Kafka Streams, Today a Kafka Streams application will implicitly create state. Whenever operations are made against this object it must first check if the underlying StateStore is valid. In addition, this leads to extra IOs to external databases/key value stores that could potentially slow down the entire pipeline. Hence, the user might not see the latest value for their data.

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