Repository: kafka Updated Branches: refs/heads/0.11.0 ec2faa179 -> 97f5525ca
MINOR: add IQ docs to streams documentation Author: Damian Guy <[email protected]> Reviewers: Matthias J. Sax <[email protected]>, Guozhang Wang <[email protected]> Closes #3484 from dguy/iq-doc Project: http://git-wip-us.apache.org/repos/asf/kafka/repo Commit: http://git-wip-us.apache.org/repos/asf/kafka/commit/97f5525c Tree: http://git-wip-us.apache.org/repos/asf/kafka/tree/97f5525c Diff: http://git-wip-us.apache.org/repos/asf/kafka/diff/97f5525c Branch: refs/heads/0.11.0 Commit: 97f5525cabbe29b17577e771e05d3daa5d6c53cd Parents: ec2faa1 Author: Damian Guy <[email protected]> Authored: Wed Jul 5 14:15:43 2017 -0700 Committer: Guozhang Wang <[email protected]> Committed: Wed Jul 5 20:12:47 2017 -0700 ---------------------------------------------------------------------- docs/images/streams-interactive-queries-01.png | Bin 0 -> 80976 bytes docs/images/streams-interactive-queries-02.png | Bin 0 -> 73218 bytes docs/images/streams-interactive-queries-03.png | Bin 0 -> 79879 bytes .../streams-interactive-queries-api-01.png | Bin 0 -> 84438 bytes .../streams-interactive-queries-api-02.png | Bin 0 -> 100725 bytes docs/streams/developer-guide.html | 518 +++++++++++++++++++ docs/streams/index.html | 1 + 7 files changed, 519 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/kafka/blob/97f5525c/docs/images/streams-interactive-queries-01.png ---------------------------------------------------------------------- diff --git a/docs/images/streams-interactive-queries-01.png b/docs/images/streams-interactive-queries-01.png new file mode 100644 index 0000000..d5d5031 Binary files /dev/null and b/docs/images/streams-interactive-queries-01.png differ http://git-wip-us.apache.org/repos/asf/kafka/blob/97f5525c/docs/images/streams-interactive-queries-02.png ---------------------------------------------------------------------- diff --git a/docs/images/streams-interactive-queries-02.png b/docs/images/streams-interactive-queries-02.png new file mode 100644 index 0000000..ea894b6 Binary files /dev/null and b/docs/images/streams-interactive-queries-02.png differ http://git-wip-us.apache.org/repos/asf/kafka/blob/97f5525c/docs/images/streams-interactive-queries-03.png ---------------------------------------------------------------------- diff --git a/docs/images/streams-interactive-queries-03.png b/docs/images/streams-interactive-queries-03.png new file mode 100644 index 0000000..403e3ae Binary files /dev/null and b/docs/images/streams-interactive-queries-03.png differ http://git-wip-us.apache.org/repos/asf/kafka/blob/97f5525c/docs/images/streams-interactive-queries-api-01.png ---------------------------------------------------------------------- diff --git a/docs/images/streams-interactive-queries-api-01.png b/docs/images/streams-interactive-queries-api-01.png new file mode 100644 index 0000000..2b4aaed Binary files /dev/null and b/docs/images/streams-interactive-queries-api-01.png differ http://git-wip-us.apache.org/repos/asf/kafka/blob/97f5525c/docs/images/streams-interactive-queries-api-02.png ---------------------------------------------------------------------- diff --git a/docs/images/streams-interactive-queries-api-02.png b/docs/images/streams-interactive-queries-api-02.png new file mode 100644 index 0000000..e5e7527 Binary files /dev/null and b/docs/images/streams-interactive-queries-api-02.png differ http://git-wip-us.apache.org/repos/asf/kafka/blob/97f5525c/docs/streams/developer-guide.html ---------------------------------------------------------------------- diff --git a/docs/streams/developer-guide.html b/docs/streams/developer-guide.html index f6a8e6a..6c75172 100644 --- a/docs/streams/developer-guide.html +++ b/docs/streams/developer-guide.html @@ -406,6 +406,524 @@ </pre> <br> + <h3><a id="streams_interactive_queries" href="#streams_interactive_queries">Interactive Queries</a></h3> + <p> + Interactive queries let you get more from streaming than just the processing of data. This feature allows you to treat the stream processing layer as a lightweight embedded database and, more concretely, <i>to directly query the latest state</i> of your stream processing application, without needing to materialize that state to external databases or external storage first. + As a result, interactive queries simplify the architecture of many use cases and lead to more application-centric architectures. For example, you often no longer need to operate and interface with a separate database cluster -- or a separate infrastructure team in your company that runs that cluster -- to share data between a Kafka Streams application (say, an event-driven microservice) and downstream applications, regardless of whether these applications use Kafka Streams or not; they may even be applications that do not run on the JVM, e.g. implemented in Python, C/C++, or JavaScript. + The following diagrams juxtapose two architectures: the first does not use interactive queries whereas the second architecture does. It depends on the concrete use case to determine which of these architectures is a better fit -- the important takeaway is that Kafka Streams and interactive queries give you the flexibility to pick and to compose the right one, rather than limiting you to just a single way. + </p> + + + <figure> + <img class="centerd" src="/{{version}}/images/streams-interactive-queries-01.png" style="width:600pt;"> + <figcaption style="text-align: center;"><i>Without interactive queries: increased complexity and heavier footprint of architecture</i></figcaption> + </figure> + + + <figure> + <img class="centered" src="/{{version}}/images/streams-interactive-queries-02.png" style="width:500pt;"> + <figcaption style="text-align: center;"><i>With interactive queries: simplified, more application-centric architecture</i></figcaption> + </figure> + + <p> + Here are some use case examples for applications that benefit from interactive queries: + </p> + <ul> + <li>Real-time monitoring: A front-end dashboard that provides threat intelligence (e.g., web servers currently + under attack by cyber criminals) can directly query a Kafka Streams application that continuously generates the + relevant information by processing network telemetry data in real-time. + </li> + <li>Video gaming: A Kafka Streams application continuously tracks location updates from players in the gaming universe. + A mobile companion app can then directly query the Kafka Streams application to show the current location of a player + to friends and family, and invite them to come along. Similarly, the game vendor can use the data to identify unusual + hotspots of players, which may indicate a bug or an operational issue. + </li> + <li>Risk and fraud: A Kafka Streams application continuously analyzes user transactions for anomalies and suspicious + behavior. An online banking application can directly query the Kafka Streams application when a user logs in to deny + access to those users that have been flagged as suspicious. + </li> + <li>Trend detection: A Kafka Streams application continuously computes the latest top charts across music genres based on + user listening behavior that is collected in real-time. Mobile or desktop applications of a music store can then + interactively query for the latest charts while users are browsing the store. + </li> + </ul> + + <h4><a id="treams_developer-guide_interactive-queries_your_app" href="#treams_developer-guide_interactive-queries_your_app">Your application and interactive queries</a></h4> + <p> + Interactive queries allow you to tap into the <i>state</i> of your application, and notably to do that from outside your application. + However, an application is not interactively queryable out of the box: you make it queryable by leveraging the API of Kafka Streams. + </p> + + <p> + It is important to understand that the state of your application -- to be extra clear, we might call it "the full state of the entire application" -- is typically split across many distributed instances of your application, and thus across many state stores that are managed locally by these application instances. + </p> + + <img class="centered" src="/{{version}}/images/streams-interactive-queries-03.png" style="width:400pt; height:400pt;"> + + <p> + Accordingly, the API to let you interactively query your application's state has two parts, a <i>local</i> and a <i>remote</i> one: + </p> + + <ol> + <li><a href="#streams_developer-guide_interactive-queries_local-stores">Querying local state stores (for an application instance)</a>: You can query that (part of the full) state that is managed locally by an instance of your application. Here, an application instance can directly query its own local state stores. You can thus use the corresponding (local) data in other parts of your application code that are not related to calling the Kafka Streams API. Querying state stores is always *read-only* to guarantee that the underlying state stores will never be mutated out-of-band, e.g. you cannot add new entries; state stores should only ever be mutated by the corresponding processor topology and the input data it operates on. + </li> + <li><a href="#streams_developer-guide_interactive-queries_discovery">Querying remote state stores (for the entire application)</a>: To query the full state of your entire application we must be able to piece together the various local fragments of the state. In addition to being able to (a) query local state stores as described in the previous bullet point, we also need to (b) discover all the running instances of your application in the network, including their respective state stores and (c) have a way to communicate with these instances over the network, i.e. an RPC layer. Collectively, these building blocks enable intra-app communcation (between instances of the same app) as well as inter-app communication (from other applications) for interactive queries. + </li> + </ol> + + <table class="data-table"> + <tbody> + <tr> + <th>What of the below is required to access the state of ...</th> + <th>... an app instance (local state)</th> + <th>... the entire application (full state)</th> + </tr> + <tr> + <td>Query local state stores of an app instance</td><td>Required (but already built-in)</td><td>Required (but already built-in)</td> + </tr> + <tr> + <td>Make an app instance discoverable to others</td><td>Not needed</td><td>Required (but already built-in)</td> + </tr> + <tr> + <td>Discover all running app instances and their state stores</td><td>Not needed</td><td>Required (but already built-in)</td> + </tr> + <tr> + <td>Communicate with app instances over the network (RPC)</td><td>Not needed</td><td>Required <b>user must provide</b></td> + </tr> + </tbody> + </table> + + <p> + Kafka Streams provides all the required functionality for interactively querying your application's state out of the box, with but one exception: if you want to expose your application's full state via interactive queries, then -- + for reasons we explain further down below -- it is your responsibility to add an appropriate RPC layer (such as a REST + API) to your application that allows application instances to communicate over the network. If, however, you only need + to let your application instances access their own local state, then you do not need to add such an RPC layer at all. + </p> + + <h4><a id="streams_developer-guide_interactive-queries_local-stores" href="#streams_developer-guide_interactive-queries_local-stores">Querying local state stores (for an application instance)</a></h4> + <p> + A Kafka Streams application is typically running on many instances. + The state that is locally available on any given instance is only a subset of the application's entire state. + Querying the local stores on an instance will, by definition, <i>only return data locally available on that particular instance</i>. + We explain how to access data in state stores that are not locally available in section <a href="#streams_developer-guide_interactive-queries_discovery">Querying remote state stores (for the entire application)</a>. + </p> + + <p> + The method <code>KafkaStreams#store(...)</code> finds an application instance's local state stores <i>by name</i> and <i>by type</i>. + </p> + + <figure> + <img class="centerd" src="/{{version}}/images/streams-interactive-queries-api-01.png" style="width:500pt;"> + <figcaption style="text-align: center;"><i>Every application instance can directly query any of its local state stores</i></figcaption> + </figure> + + <p> + The <i>name</i> of a state store is defined when you are creating the store, either when creating the store explicitly (e.g. when using the Processor API) or when creating the store implicitly (e.g. when using stateful operations in the DSL). + We show examples of how to name a state store further down below. + </p> + + <p> + The <i>type</i> of a state store is defined by <code>QueryableStoreType</code>, and you can access the built-in types via the class <code>QueryableStoreTypes</code>. + Kafka Streams currently has two built-in types: + </p> + <ul> + <li>A key-value store <code>QueryableStoreTypes#keyValueStore()</code>, see <a href="#streams_developer-guide_interactive-queries_local-key-value-stores">Querying local key-value stores</a>.</li> + <li>A window store <code>QueryableStoreTypes#windowStore()</code>, see <a href="#streams_developer-guide_interactive-queries_local-window-stores">Querying local window stores</a>.</li> + </ul> + + <p> + Both store types return <i>read-only</i> versions of the underlying state stores. + This read-only constraint is important to guarantee that the underlying state stores will never be mutated (e.g. new entries added) out-of-band, i.e. only the corresponding processing topology of Kafka Streams is allowed to mutate and update the state stores in order to ensure data consistency. + </p> + <p> + You can also implement your own <code>QueryableStoreType</code> as described in section <a href="#streams_developer-guide_interactive-queries_custom-stores#">Querying local custom stores</a> + </p> + + <p> + Kafka Streams materializes one state store per stream partition, which means your application will potentially manage many underlying state stores. + The API to query local state stores enables you to query all of the underlying stores without having to know which partition the data is in. + The objects returned from <code>KafkaStreams#store(...)</code> are therefore wrapping potentially many underlying state stores. + </p> + + <h4><a id="streams_developer-guide_interactive-queries_local-key-value-stores" href="#streams_developer-guide_interactive-queries_local-key-value-stores">Querying local key-value stores</a></h4> + <p> + To query a local key-value store, you must first create a topology with a key-value store: + </p> + + <pre class="brush: java;"> + StreamsConfig config = ...; + KStreamBuilder builder = ...; + KStream<String, String> textLines = ...; + + // Define the processing topology (here: WordCount) + KGroupedStream<String, String> groupedByWord = textLines + .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+"))) + .groupBy((key, word) -> word, stringSerde, stringSerde); + + // Create a key-value store named "CountsKeyValueStore" for the all-time word counts + groupedByWord.count("CountsKeyValueStore"); + + // Start an instance of the topology + KafkaStreams streams = new KafkaStreams(builder, config); + streams.start(); + </pre> + + <p> + Above we created a key-value store named "CountsKeyValueStore". + This store will hold the latest count for any word that is found on the topic "word-count-input". + Once the application has started we can get access to "CountsKeyValueStore" and then query it via the <code>ReadOnlyKeyValueStore</code> API: + </p> + + <pre class="brush: java;"> + // Get the key-value store CountsKeyValueStore + ReadOnlyKeyValueStore<String, Long> keyValueStore = + streams.store("CountsKeyValueStore", QueryableStoreTypes.keyValueStore()); + + // Get value by key + System.out.println("count for hello:" + keyValueStore.get("hello")); + + // Get the values for a range of keys available in this application instance + KeyValueIterator<String, Long> range = keyValueStore.range("all", "streams"); + while (range.hasNext()) { + KeyValue<String, Long> next = range.next(); + System.out.println("count for " + next.key + ": " + value); + } + + // Get the values for all of the keys available in this application instance + KeyValueIterator<String, Long> range = keyValueStore.all(); + while (range.hasNext()) { + KeyValue<String, Long> next = range.next(); + System.out.println("count for " + next.key + ": " + value); + } + </pre> + + <h4><a id="streams_developer-guide_interactive-queries_local-window-stores" href="#streams_developer-guide_interactive-queries_local-window-stores">Querying local window stores</a></h4> + <p> + A window store differs from a key-value store in that you will potentially have many results for any given key because the key can be present in multiple windows. + However, there will ever be at most one result per window for a given key. + </p> + <p> + To query a local window store, you must first create a topology with a window store: + </p> + + <pre class="brush: java;"> + StreamsConfig config = ...; + KStreamBuilder builder = ...; + KStream<String, String> textLines = ...; + + // Define the processing topology (here: WordCount) + KGroupedStream<String, String> groupedByWord = textLines + .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+"))) + .groupBy((key, word) -> word, stringSerde, stringSerde); + + // Create a window state store named "CountsWindowStore" that contains the word counts for every minute + groupedByWord.count(TimeWindows.of(60000), "CountsWindowStore"); + </pre> + + <p> + Above we created a window store named "CountsWindowStore" that contains the counts for words in 1-minute windows. + Once the application has started we can get access to "CountsWindowStore" and then query it via the <code>ReadOnlyWindowStore</code> API: + </p> + + <pre class="brush: java;"> + // Get the window store named "CountsWindowStore" + ReadOnlyWindowStore<String, Long> windowStore = + streams.store("CountsWindowStore", QueryableStoreTypes.windowStore()); + + // Fetch values for the key "world" for all of the windows available in this application instance. + // To get *all* available windows we fetch windows from the beginning of time until now. + long timeFrom = 0; // beginning of time = oldest available + long timeTo = System.currentTimeMillis(); // now (in processing-time) + WindowStoreIterator<Long> iterator = windowStore.fetch("world", timeFrom, timeTo); + while (iterator.hasNext()) { + KeyValue<Long, Long> next = iterator.next(); + long windowTimestamp = next.key; + System.out.println("Count of 'world' @ time " + windowTimestamp + " is " + next.value); + } + </pre> + + <h4><a id="streams_developer-guide_interactive-queries_custom-stores" href="#streams_developer-guide_interactive-queries_custom-stores">Querying local custom state stores</a></h4> + <p> + Any custom state stores you use in your Kafka Streams applications can also be queried. + However there are some interfaces that will need to be implemented first: + </p> + + <ol> + <li>Your custom state store must implement <code>StateStore</code>.</li> + <li>You should have an interface to represent the operations available on the store.</li> + <li>It is recommended that you also provide an interface that restricts access to read-only operations so users of this API can't mutate the state of your running Kafka Streams application out-of-band.</li> + <li>You also need to provide an implementation of <code>StateStoreSupplier</code> for creating instances of your store.</li> + </ol> + + <p> + The class/interface hierarchy for your custom store might look something like: + </p> + + <pre class="brush: java;"> + public class MyCustomStore<K,V> implements StateStore, MyWriteableCustomStore<K,V> { + // implementation of the actual store + } + + // Read-write interface for MyCustomStore + public interface MyWriteableCustomStore<K,V> extends MyReadableCustomStore<K,V> { + void write(K Key, V value); + } + + // Read-only interface for MyCustomStore + public interface MyReadableCustomStore<K,V> { + V read(K key); + } + + public class MyCustomStoreSupplier implements StateStoreSupplier { + // implementation of the supplier for MyCustomStore + } + </pre> + + <p> + To make this store queryable you need to: + </p> + <ul> + <li>Provide an implementation of <code>QueryableStoreType</code>.</li> + <li>Provide a wrapper class that will have access to all of the underlying instances of the store and will be used for querying.</li> + </ul> + + <p> + Implementing <code>QueryableStoreType</code> is straight forward: + </p> + + <pre class="brush: java;"> + + public class MyCustomStoreType<K,V> implements QueryableStoreType<MyReadableCustomStore<K,V>> { + + // Only accept StateStores that are of type MyCustomStore + public boolean accepts(final StateStore stateStore) { + return stateStore instanceOf MyCustomStore; + } + + public MyReadableCustomStore<K,V> create(final StateStoreProvider storeProvider, final String storeName) { + return new MyCustomStoreTypeWrapper(storeProvider, storeName, this); + } + + } + </pre> + + <p> + A wrapper class is required because even a single instance of a Kafka Streams application may run multiple stream tasks and, by doing so, manage multiple local instances of a particular state store. + The wrapper class hides this complexity and lets you query a "logical" state store with a particular name without having to know about all of the underlying local instances of that state store. + </p> + + <p> + When implementing your wrapper class you will need to make use of the <code>StateStoreProvider</code> + interface to get access to the underlying instances of your store. + <code>StateStoreProvider#stores(String storeName, QueryableStoreType<T> queryableStoreType)</code> returns a <code>List</code> of state stores with the given <code>storeName</code> and of the type as defined by <code>queryableStoreType</code>. + </p> + <p> + An example implemention of the wrapper follows (Java 8+): + </p> + + <pre class="brush: java;"> + // We strongly recommended implementing a read-only interface + // to restrict usage of the store to safe read operations! + public class MyCustomStoreTypeWrapper<K,V> implements MyReadableCustomStore<K,V> { + + private final QueryableStoreType<MyReadableCustomStore<K, V>> customStoreType; + private final String storeName; + private final StateStoreProvider provider; + + public CustomStoreTypeWrapper(final StateStoreProvider provider, + final String storeName, + final QueryableStoreType<MyReadableCustomStore<K, V>> customStoreType) { + + // ... assign fields ... + } + + // Implement a safe read method + @Override + public V read(final K key) { + // Get all the stores with storeName and of customStoreType + final List<MyReadableCustomStore<K, V>> stores = provider.getStores(storeName, customStoreType); + // Try and find the value for the given key + final Optional<V> value = stores.stream().filter(store -> store.read(key) != null).findFirst(); + // Return the value if it exists + return value.orElse(null); + } + } + </pre> + + <p> + Putting it all together you can now find and query your custom store: + </p> + + <pre class="brush: java;"> + StreamsConfig config = ...; + TopologyBuilder builder = ...; + ProcessorSupplier processorSuppler = ...; + + // Create CustomStoreSupplier for store name the-custom-store + MyCustomStoreSuppler customStoreSupplier = new MyCustomStoreSupplier("the-custom-store"); + // Add the source topic + builder.addSource("input", "inputTopic"); + // Add a custom processor that reads from the source topic + builder.addProcessor("the-processor", processorSupplier, "input"); + // Connect your custom state store to the custom processor above + builder.addStateStore(customStoreSupplier, "the-processor"); + + KafkaStreams streams = new KafkaStreams(builder, config); + streams.start(); + + // Get access to the custom store + MyReadableCustomStore<String,String> store = streams.store("the-custom-store", new MyCustomStoreType<String,String>()); + // Query the store + String value = store.read("key"); + </pre> + + <h4><a id="streams_developer-guide_interactive-queries_discovery" href="#streams_developer-guide_interactive-queries_discovery">Querying remote state stores (for the entire application)</a></h4> + + <p> + Typically, the ultimate goal for interactive queries is not to just query locally available state stores from within an instance of a Kafka Streams application as described in the previous section. + Rather, you want to expose the application's full state (i.e. the state across all its instances) to other applications that might be running on different machines. + For example, you might have a Kafka Streams application that processes the user events in a multi-player video game, and you want to retrieve the latest status of each user directly from this application so that you can display it in a mobile companion app. + </p> + <p> + Three steps are needed to make the full state of your application queryable: + </p> + + <ol> + <li>You must <a href="#streams_developer-guide_interactive-queries_rpc-layer">add an RPC layer to your application</a> so that the instances of your application may be interacted with via the network -- notably to respond to interactive queries. + By design Kafka Streams does not provide any such RPC functionality out of the box so that you can freely pick your favorite approach: a REST API, Thrift, a custom protocol, and so on.</li> + <li>You need to <a href="#streams_developer-guide_interactive-queries_expose-rpc">expose the respective RPC endpoints</a> of your application's instances via the <code>application.server</code> configuration setting of Kafka Streams. + Because RPC endpoints must be unique within a network, each instance will have its own value for this configuration setting. + This makes an application instance discoverable by other instances.</li> + <li> In the RPC layer, you can then <a href="#streams_developer-guide_interactive-queries_discover-app-instances-and-stores">discover remote application instances</a> and their respective state stores (e.g. for forwarding queries to other app instances if an instance lacks the local data to respond to a query) as well as <a href="#streams_developer-guide_interactive-queries_local-stores">query locally available state stores</a> (in order to directly respond to queries) in order to make the full state of your application queryable.</li> + </ol> + + <figure> + <img class="centered" src="/{{version}}/images/streams-interactive-queries-api-02.png" style="width:500pt;"> + <figcaption style="text-align: center;"><i>Discover any running instances of the same application as well as the respective RPC endpoints they expose for interactive queries</i></figcaption> + </figure> + + <h4><a id="streams_developer-guide_interactive-queries_rpc-layer" href="#streams_developer-guide_interactive-queries_rpc-layer">Adding an RPC layer to your application</a></h4> + <p> + As Kafka Streams doesn't provide an RPC layer you are free to choose your favorite approach. + There are many ways of doing this, and it will depend on the technologies you have chosen to use. + The only requirements are that the RPC layer is embedded within the Kafka Streams application and that it exposes an endpoint that other application instances and applications can connect to. + </p> + + <h4><a id="streams_developer-guide_interactive-queries_expose-rpc" href="#streams_developer-guide_interactive-queries_expose-rpc">Exposing the RPC endpoints of your application</a></h4> + <p> + To enable the remote discovery of state stores running within a (typically distributed) Kafka Streams application you need to set the <code>application.server</code> configuration property in <code>StreamsConfig</code>. + The <code>application.server</code> property defines a unique <code>host:port</code> pair that points to the RPC endpoint of the respective instance of a Kafka Streams application. + It's important to understand that the value of this configuration property varies across the instances of your application. + When this property is set, then, for every instance of an application, Kafka Streams will keep track of the instance's RPC endpoint information, its state stores, and assigned stream partitions through instances of <code>StreamsMetadata</code> + </p> + <p> + Below is an example of configuring and running a Kafka Streams application that supports the discovery of its state stores. + </p> + + <pre class="brush: java;"> + + Properties props = new Properties(); + // Set the unique RPC endpoint of this application instance through which it + // can be interactively queried. In a real application, the value would most + // probably not be hardcoded but derived dynamically. + String rpcEndpoint = "host1:4460"; + props.put(StreamsConfig.APPLICATION_SERVER_CONFIG, rpcEndpoint); + // ... further settings may follow here ... + + StreamsConfig config = new StreamsConfig(props); + KStreamBuilder builder = new KStreamBuilder(); + + KStream<String, String> textLines = builder.stream(stringSerde, stringSerde, "word-count-input"); + + KGroupedStream<String, String> groupedByWord = textLines + .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+"))) + .groupBy((key, word) -> word, stringSerde, stringSerde); + + // This call to `count()` creates a state store named "word-count". + // The state store is discoverable and can be queried interactively. + groupedByWord.count("word-count"); + + // Start an instance of the topology + KafkaStreams streams = new KafkaStreams(builder, streamsConfiguration); + streams.start(); + + // Then, create and start the actual RPC service for remote access to this + // application instance's local state stores. + // + // This service should be started on the same host and port as defined above by + // the property `StreamsConfig.APPLICATION_SERVER_CONFIG`. The example below is + // fictitious, but we provide end-to-end demo applications (such as KafkaMusicExample) + // that showcase how to implement such a service to get you started. + MyRPCService rpcService = ...; + rpcService.listenAt(rpcEndpoint); + </pre> + + <h4><a id="streams_developer-guide_interactive-queries_discover-app-instances-and-stores" href="#streams_developer-guide_interactive-queries_discover-app-instances-and-stores">Discovering and accessing application instances and their respective local state stores</a></h4> + <p> + With the <code>application.server</code> property set, we can now find the locations of remote app instances and their state stores. + The following methods return <code>StreamsMetadata</code> objects, which provide meta-information about application instances such as their RPC endpoint and locally available state stores. + </p> + <ul> + <li><code>KafkaStreams#allMetadata()</code>: find all instances of this application</li> + <li><code>KafkaStreams#allMetadataForStore(String storeName)</code>: find those applications instances that manage local instances of the state store "storeName"</li> + <li><code>KafkaStreams#metadataForKey(String storeName, K key, Serializer<K> keySerializer)</code>: using the default stream partitioning strategy, find the one application instance that holds the data for the given key in the given state store</li> + <li><code>KafkaStreams#metadataForKey(String storeName, K key, StreamPartitioner<K, ?> partitioner)</code>: using <code>>partitioner</code>, find the one application instance that holds the data for the given key in the given state store</li> + </ul> + + <p> + If <code>application.server</code> is not configured for an application instance, then the above methods will not find any <code>StreamsMetadata</code> for it. + </p> + + <p> + For example, we can now find the <code>StreamsMetadata</code> for the state store named "word-count" that we defined in the code example shown in the previous section: + </p> + + <pre class="brush: java;"> + + KafkaStreams streams = ...; + // Find all the locations of local instances of the state store named "word-count" + Collection<StreamsMetadata> wordCountHosts = streams.allMetadataForStore("word-count"); + + // For illustrative purposes, we assume using an HTTP client to talk to remote app instances. + HttpClient http = ...; + + // Get the word count for word (aka key) 'alice': Approach 1 + // + // We first find the one app instance that manages the count for 'alice' in its local state stores. + StreamsMetadata metadata = streams.metadataForKey("word-count", "alice", Serdes.String().serializer()); + // Then, we query only that single app instance for the latest count of 'alice'. + // Note: The RPC URL shown below is fictitious and only serves to illustrate the idea. Ultimately, + // the URL (or, in general, the method of communication) will depend on the RPC layer you opted to + // implement. Again, we provide end-to-end demo applications (such as KafkaMusicExample) that showcase + // how to implement such an RPC layer. + Long result = http.getLong("http://"; + metadata.host() + ":" + metadata.port() + "/word-count/alice"); + + // Get the word count for word (aka key) 'alice': Approach 2 + // + // Alternatively, we could also choose (say) a brute-force approach where we query every app instance + // until we find the one that happens to know about 'alice'. + Optional<Long> result = streams.allMetadataForStore("word-count") + .stream() + .map(streamsMetadata -> { + // Construct the (fictituous) full endpoint URL to query the current remote application instance + String url = "http://"; + streamsMetadata.host() + ":" + streamsMetadata.port() + "/word-count/alice"; + // Read and return the count for 'alice', if any. + return http.getLong(url); + }) + .filter(s -> s != null) + .findFirst(); + </pre> + + <p> + At this point the full state of the application is interactively queryable: + </p> + <ul> + <li>We can discover the running instances of the application as well as the state stores they manage locally.</li> + <li>Through the RPC layer that was added to the application, we can communicate with these application instances over the network and query them for locally available state</li> + <li>The application instances are able to serve such queries because they can directly query their own local state stores and respond via the RPC layer</li> + <li>Collectively, this allows us to query the full state of the entire application</li> + </ul> + <h3><a id="streams_configure_execute" href="#streams_configure_execute">Application Configuration and Execution</a></h3> <p> http://git-wip-us.apache.org/repos/asf/kafka/blob/97f5525c/docs/streams/index.html ---------------------------------------------------------------------- diff --git a/docs/streams/index.html b/docs/streams/index.html index 2418497..013494b 100644 --- a/docs/streams/index.html +++ b/docs/streams/index.html @@ -32,6 +32,7 @@ <ul> <li><a href="/{{version}}/documentation/streams/developer-guide#streams_processor">Low-level Processor API</a></li> <li><a href="/{{version}}/documentation/streams/developer-guide#streams_dsl">High-level Streams DSL</a></li> + <li><a href="/{{version}}/documentation/streams/developer-guide#streams_interactive_querie">Interactive Queries</a></li> <li><a href="/{{version}}/documentation/streams/developer-guide#streams_execute">Application Configuration and Execution</a></li> </ul> </li>
