Integrate Solr with real-time stream processing applications
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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
h"p://www.linkedin.com/in/thelabdude
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)
h"p://solarsystem.nasa.gov/mul8media/display.cfm?Category=Spacecra?&IM_ID=17966
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 ...
SpringBolt SpringSpout
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
callback to receive messages from Netty HttpClient
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
Twi"er 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: h"p://deals.manningpublica8ons.com/Revolu8onsEU2013.html
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