FROM LEGACY, TO BATCH, TO NEAR REAL-TIME. Marc Sturlese, Dani Solà
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1 FROM LEGACY, TO BATCH, TO NEAR REAL-TIME Marc Sturlese, Dani Solà
2 WHO ARE WE? Marc Sturlese Backend engineer, focused on R&D Interests: search, scalability Dani Solà Backend engineer Interests: distributed systems, data mining, search,...
3 TROVIT Search engine for classifieds: 6 verticals, 38 countries & growing
4
5 FROM LEGACY TO BATCH Old architecture Why & when we changed Current architecture Hive, Pig & custom tools Migration process
6 OLD ARCHITECTURE Based on MySQL and PHP scripts Indexes created with DataImportHandler Incoming data DataImportHandler MySQL Lucene Indexes PHP Scripts
7 WHEN & WHY WE MOVED Sharded strategies are hard to maintain We had 10M rows in a single table Many processes working on MySQL databases We wanted a more maintainable codebase The solution was pretty obvious...
8 CURRENT ARCHITECTURE Based on Hadoop Batch process that reprocess all the ads... But needs to be aware of the previous execution! Hive & custom tools to know what happens
9 CURRENT ARCHITECTURE Incoming data External Data Lucene Indexes Deployment Ad Processor Diff Matching Expiration Deduplication Indexing t - 1 Hadoop Cluster Hive Stats
10 AD PROCESSOR Incoming data Converts text files to Thrift objects Checks that the ads are complete Searches for poisonwords Ad Processor Thrift Objects Checks the value ranges Parses text (dates, currencies, etc)
11 DIFF PHASE ads t ads t-1 Performs the diff between executions Diff Merges the ads of both executions ads t
12 MATCHING PHASE ads External Data Extracts semantic information: Geographical information Matching Cars makes and models Companies enriched ads...
13 EXPIRATION PHASE ads Works as a filter Deletes: Expiration Expired ads Incorrect ads ads to be indexed
14 DEDUPLICATION PHASE Duplicates are a big issue for us ads You cannot compare N ads against each other Solution: Deduplication deduplicated ads Use heuristics to create possible duplicates groups Compare all the ads of each group
15 INDEXING PHASE ads Is actually done with two phases First we create micro indexes We use Embedded Solr Server Expiration Then we merge them Plain Lucene Lucene Indexes
16 HIVE, PIG & CUSTOM TOOLS Critical: To know what is going on (control info) To debug To prototype new processes grep, cat To understand your data To create reports
17 MIGRATION PROCESS Used Amazon EC2 to test different cluster configurations Maintained both systems running during one month Switched to the new system gradually, one country at a time Then we moved the cluster to our own servers
18 FROM BATCH TO NEAR REAL-TIME Batch is not enough Storm for real time data processing HBase for data storage Zookeeper for systems coordination Putting it all together Batch and NRT. Mixed architecture
19 BATCH IS NOT ENOUGH Data processing with map reduce scales well but takes time and has latency Crunch documents in batch means wait until all is processed We want to show the user fresher results!
20 BATCH IS NOT ENOUGH ZK MR pipeline HDFS Id tables Storm + HBase + Zookeeper looks like a good feed!!! Topology Solr ZK Feeds Spouts Bolts Bolts Slaves
21 STORM - PROPERTIES Distributed real time computation system Fault tolerance Horizontal scalability Low latency Reliability
22 STORM - COMPONENTS Tuple Stream Spout Bolt Topology
23 STORM IN ACTION Spouts Bolts Bolts Streams of tuples Queue Topology DataStore
24 STORM - DAEMONS Nimbus Supervisors Workers
25 HBASE - PROPERTIES Distributed, sorted map datastore Automatic failover Rows are sorted Many columns per row Good Hadoop integration
26 HBASE - COMPONENTS Master Slave coordination and failure detection Admin features Region server (slaves)
27 ZOOKEEPER Highly available coordination system Used for locking, distributed configuration, leader election, cluster management... Curator makes it easy for common algorithms
28 PUTTING IT ALL TOGETHER ZK MR pipeline HDFS Id tables Solr Topology ZK Feeds Spouts Bolts processor Bolt Indexer Slaves
29 MIXED ARCHITECTURE If the number of segments in the index gets too big is has an impact in search performance Building indexes in batch allows to keep small number of segments Gives near real time updates and it s tolerant to human error
30 THANK YOU! QUESTIONS?
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