Integrate Solr with real-time stream processing applications
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Integrate Solr with real-time stream processing applications

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Storm is a real-time distributed computation system used to process massive streams of data. Many organizations are turning to technologies like Storm to complement batch-oriented big data ...

Storm is a real-time distributed computation system used to process massive streams of data. Many organizations are turning to technologies like Storm to complement batch-oriented big data technologies, such as Hadoop, to deliver time-sensitive analytics at scale. This talk introduces on an emerging architectural pattern of integrating Solr and Storm to process big data in real time. There are a number of natural integration points between Solr and Storm, such as populating a Solr index or supplying data to Storm using Solr’s real-time get support. In this session, Timothy will cover the basic concepts of Storm, such as spouts and bolts. He’ll then provide examples of how to integrate Solr into Storm to perform large-scale indexing in near real-time. In addition, we'll see how to embed Solr in a Storm bolt to match incoming tuples against pre-configured queries, commonly known as percolator. Attendees will come away from this presentation with a good introduction to stream processing technologies and several real-world use cases of how to integrate Solr with Storm.

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    Integrate Solr with real-time stream processing applications Integrate Solr with real-time stream processing applications Presentation Transcript

    • INTEGRATE SOLR WITH REAL-TIME STREAM PROCESSING APPLICATIONS Timothy Potter @thelabdude linkedin.com/thelabdude
    • whoami independent consultant search / big data projects soon to be joining engineering team @LucidWorks co-author Solr In Action previously big data architect Dachis Group
    • my storm story re-designed a complex batch-oriented indexing pipeline based on Hadoop (Oozie, Pig, Hive, Sqoop) to real-time storm topology
    • agenda walk through how to develop a storm topology common integration points with Solr (near real-time indexing, percolator, real-time get)
    • example listen to click events from 1.usa.gov URL shortener (bit.ly) to determine trending US government sites stream of click events: http://developer.usa.gov/1usagov http://www.smartgrid.gov -> http://1.usa.gov/ayu0Ru
    • beyond word count tackle real challenges you’ll encounter when developing a storm topology and what about ... unit testing, dependency injection, measure runtime behavior of your components, separation of concerns, reducing boilerplate, hiding complexity ...
    • storm open source distributed computation system scalability, fault-tolerance, guaranteed message processing (optional)
    • storm primitives • tuple: ordered list of values • stream: unbounded sequence of tuples • spout: emit a stream of tuples (source) • bolt: performs some operation on each tuple • topology: dag of spouts and tuples
    • solution requirements • receive click events from 1.usa.gov stream • count frequency of pages in a time window • rank top N sites per time window • extract title, body text, image for each link • persist rankings and metadata for visualization
    • trending snapshot (sept 12, 2013)
    • Solr Metrics DB EnrichLink Bolt Solr Indexing Bolt 1.usa.gov Spout Rolling Count Bolt Intermediate Rankings Bolt Total Rankings Bolt embed.ly API field grouping bit.ly hash field grouping bit.ly hash global grouping Persist Rankings Bolt field grouping obj global grouping provided by in the storm-starter project data store bolt spout stream grouping
    • stream grouping • shuffle: random distribution of tuples to all instances of a bolt • field(s): group tuples by one or more fields in common • global: reduce down to one • all: replicate stream to all instances of a bolt source: https://github.com/nathanmarz/storm/wiki/Concepts
    • useful storm concepts • bolts can receive input from many spouts • tuples in a stream can be grouped together • streams can be split and joined • bolts can inject new tuples into the stream • components can be distributed across a cluster at a configurable parallelism level • optionally, storm keeps track of each tuple emitted by a spout (ack or fail)
    • tools • Spring framework – dependency injection, configuration, unit testing, mature, etc. • Groovy – keeps your code tidy and elegant • Mockito – ignore stuff your test doesn’t care about • Netty – fast & powerful NIO networking library • Coda Hale metrics – get visibility into how your bolts and spouts are performing (at a very low-level)
    • spout easy! just produce a stream of tuples ... and ... avoid blocking when waiting for more data, ease off throttle if topology is not processing fast enough, deal with failed tuples, choose if it should use message Ids for each tuple emitted, data model / schema, etc ...
    • SpringBoltSpringSpout Streaming DataAction (POJO) Streaming DataProvider (POJO) Spring container (1 per topology per JVM) Spring Dependency Injection JDBC WebService Hide complexity of implementing Storm contract developer focuses on business logic
    • streaming data provider class OneUsaGovStreamingDataProvider implements StreamingDataProvider, MessageHandler { MessageStream messageStream ... void open(Map stormConf) { messageStream.receive(this) } boolean next(NamedValues nv) { String msg = queue.poll() if (msg) { OneUsaGovRequest req = objectMapper.readValue(msg, OneUsaGovRequest) if (req != null && req.globalBitlyHash != null) { nv.set(OneUsaGovTopology.GLOBAL_BITLY_HASH, req.globalBitlyHash) nv.set(OneUsaGovTopology.JSON_PAYLOAD, req) return true } } return false } void handleMessage(String msg) { queue.offer(msg) } Spring Dependency Injection non-blocking call to get the next message from 1.usa.gov use Jackson JSON parser to create an object from the raw incoming data
    • jackson json to java @JsonIgnoreProperties(ignoreUnknown = true) class OneUsaGovRequest implements Serializable { @JsonProperty("a") String userAgent; @JsonProperty("c") String countryCode; @JsonProperty("nk") int knownUser; @JsonProperty("g") String globalBitlyHash; @JsonProperty("h") String encodingUserBitlyHash; @JsonProperty("l") String encodingUserLogin; ... } Spring converts json to java object for you: <bean id="restTemplate" class="org.springframework.web.client.RestTemplate"> <property name="messageConverters"> <list> <bean id="messageConverter” class="...json.MappingJackson2HttpMessageConverter"> </bean> </list> </property> </bean>
    • spout data provider spring-managed bean <bean id="oneUsaGovStreamingDataProvider" class="com.bigdatajumpstart.storm.OneUsaGovStreamingDataProvider"> <property name="messageStream"> <bean class="com.bigdatajumpstart.netty.HttpClient"> <constructor-arg index="0" value="${streamUrl}"/> </bean> </property> </bean> builder.setSpout("1.usa.gov-spout", new SpringSpout("oneUsaGovStreamingDataProvider", spoutFields), 1) Note: when building the StormTopology to submit to Storm, you do:
    • class OneUsaGovStreamingDataProviderTest extends StreamingDataProviderTestBase { @Test void testDataProvider() { String jsonStr = '''{ "a": "user-agent", "c": "US", "nk": 0, "tz": "America/Los_Angeles", "gr": "OR", "g": "2BktiW", "h": "12Me4B2", "l": "usairforce", "al": "en-us", "hh": "1.usa.gov", "r": "http://example.com/foo", ... }''' OneUsaGovStreamingDataProvider dataProvider = new OneUsaGovStreamingDataProvider() dataProvider.setMessageStream(mock(MessageStream)) dataProvider.open(stormConf) // Config setup in base class dataProvider.handleMessage(jsonStr) NamedValues record = new NamedValues(OneUsaGovTopology.spoutFields) assertTrue dataProvider.next(record) ... } } spout data provider unit test mock json to simulate data from 1.usa.gov feed use Mockito to satisfy dependencies not needed for this test asserts to verify data provider works correctly
    • rolling count bolt • counts frequency of links in a sliding time window • emits topN in current window every M seconds • uses tick tuple trick provided by Storm to emit every M seconds (configurable) • provided with the storm-starter project http://www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/
    • • calls out to embed.ly API • caches results locally in the bolt instance • relies on field grouping (incoming tuples) • outputs data to be indexed in Solr • benefits from parallelism to enrich more links concurrently (watch those rate limits) enrich link metadata bolt
    • embed.ly service class EmbedlyService { @Autowired RestTemplate restTemplate String apiKey private Timer apiTimer = MetricsSupport.timer(EmbedlyService, "apiCall") Embedly getLinkMetadata(String link) { String urlEncoded = URLEncoder.encode(link,"UTF-8") URI uri = new URI("https://api.embed.ly/1/oembed?key=${apiKey}&url=${urlEncoded}") Embedly embedly = null MetricsSupport.withTimer(apiTimer, { embedly = restTemplate.getForObject(uri, Embedly) }) return embedly } simple closure to time our requests to the Web service integrate coda hale metrics
    • • capture runtime behavior of the components in your topology • Coda Hale metrics - http://metrics.codahale.com/ • output metrics every N minutes • report metrics to JMX, ganglia, graphite, etc metrics
    • -- Meters ---------------------------------------------------------------------- EnrichLinkBoltLogic.solrQueries count = 97 mean rate = 0.81 events/second 1-minute rate = 0.89 events/second 5-minute rate = 1.62 events/second 15-minute rate = 1.86 events/second SolrBoltLogic.linksIndexed count = 60 mean rate = 0.50 events/second 1-minute rate = 0.41 events/second 5-minute rate = 0.16 events/second 15-minute rate = 0.06 events/second -- Timers ---------------------------------------------------------------------- EmbedlyService.apiCall count = 60 mean rate = 0.50 calls/second 1-minute rate = 0.40 calls/second 5-minute rate = 0.16 calls/second 15-minute rate = 0.06 calls/second min = 138.70 milliseconds max = 7642.92 milliseconds mean = 1148.29 milliseconds stddev = 1281.40 milliseconds median = 652.83 milliseconds 75% <= 1620.96 milliseconds ...
    • storm cluster concepts • nimbus: master node (~job tracker in Hadoop) • zookeeper: cluster management / coordination • supervisor: one per node in the cluster to manage worker processes • worker: one or more per supervisor (JVM process) • executor: thread in worker • task: work performed by a spout or bolt
    • Worker 1 (port 6701) Nimbus Supervisor (1 per node) Topology JAR Node 1 JVM process executor (thread) ... N workers ... M nodes Each component (spout or bolt) is distributed across a cluster of workers based on a configurable parallelism Zookeeper
    • @Override StormTopology build(StreamingApp app) throws Exception { ... TopologyBuilder builder = new TopologyBuilder() builder.setSpout("1.usa.gov-spout", new SpringSpout("oneUsaGovStreamingDataProvider", spoutFields), 1) builder.setBolt("enrich-link-bolt", new SpringBolt("enrichLinkAction", enrichedLinkFields), 3) .fieldsGrouping("1.usa.gov-spout", globalBitlyHashGrouping) ... parallelism hint to the framework (can be rebalanced)
    • solr integration points • real-time get • near real-time indexing (NRT) • percolate (match incoming docs to pre-existing queries)
    • real-time get use Solr for fast lookups by document ID class SolrClient { @Autowired SolrServer solrServer SolrDocument get(String docId, String... fields) { SolrQuery q = new SolrQuery() q.setRequestHandler("/get") q.set("id", docId) q.setFields(fields) QueryRequest req = new QueryRequest(q) req.setResponseParser(new BinaryResponseParser()) QueryResponse rsp = req.process(solrServer) return (SolrDocument)rsp.getResponse().get("doc") } } send the request to the “get” request handler
    • near real-time indexing • If possible, use CloudSolrServer to route documents directly to the correct shard leaders (SOLR-4816) • Use <openSearcher>false</openSearcher> for auto “hard” commits • Use auto soft commits as needed • Use parallelism of Storm bolt to distribute indexing work to N nodes http://searchhub.org/2013/08/23/understanding-transaction-logs-softcommit-and-commit-in-sorlcloud/
    • percolate • match incoming documents to pre-configured queries (inverted search) – example: Is this tweet related to campaign Y for brand X? • use storm’s distributed computation support to evaluate M pre-configured queries per doc
    • two possible approaches • Lucene-only solution using MemoryIndex – See presentation by Charlie Hull and Alan Woodward • EmbeddedSolrServer – Full solrconfig.xml / schema.xml – RAMDirectory – Relies on Storm to scale up documents / second – Easy solution for up to a few thousand queries
    • Twitter Spout PercolatorBolt 1 Embedded SolrServer Pre-configured queries stored in a database PercolatorBolt N Embedded SolrServer ... Could be 100’s of these random stream grouping ZeroMQ pub/sub to push query changes to percolator
    • tick tuples • send a special kind of tuple to a bolt every N seconds if (TupleHelpers.isTickTuple(input)) { // do special work } used in percolator to delete accumulated documents every minute or so ...
    • references • Storm Wiki • https://github.com/nathanmarz/storm/wiki/Documentation • Overview: Krishna Gade • http://www.slideshare.net/KrishnaGade2/storm-at-twitter • Trending Topics: Michael Knoll • http://www.michael-noll.com/blog/2013/01/18/implementing-real-time- trending-topics-in-storm/ • Understanding Parallelism: Michael Knoll • http://www.michael-noll.com/blog/2012/10/16/understanding-the- parallelism-of-a-storm-topology/
    • get the code: https://github.com/thelabdude/lsrdublin Q & A Manning coupon codes for conference related books: http://deals.manningpublications.com/RevolutionsEU2013.html