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Large scale, interactive ad-hoc queries over different datastores with Apache Drill - Michael Hausenblas (MapR technologies)
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Large scale, interactive ad-hoc queries over different datastores with Apache Drill - Michael Hausenblas (MapR technologies)


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Presented at JAX London 2013 …

Presented at JAX London 2013

Apache Drill is a distributed system for interactive ad-hoc query and analysis of large-scale datasets. It is the Open Source version of Google’s Dremel technology. Apache Drill is designed to scale to thousands of servers and able to process Petabytes of data in seconds, enabling SQL-on-Hadoop and supporting a variety of data sources.

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  • (This is a ASR-35 at DEC mainframe–other console terminals used were Teletype model 35 Teletypes)Allowing the user to issue ad-hoc queries is essential: often, the user might not necessarily know ahead of time what queries to issue. Also, one may need to react to changing circumstances. The lack of tools to perform interactive ad-hoc analysis at scale is a gap that Apache Drill fills.
  • Hive: compile to MR, Aster: external tables in MPP, Oracle/MySQL: export MR results to RDBMSDrill, Impala, CitusDB: real-time
  • Suppose a marketing analyst trying to experiment with ways to do targeting of user segments for next campaign. Needs access to web logs stored in Hadoop, and also needs to access user profiles stored in MongoDB as well as access to transaction data stored in a conventional database.
  • Geo-spatial + time series data with highly discriminative queries (timeframe, region, etc.)
  • Re ad-hoc:You might not know ahead of time what queries you will want to make. You may need to react to changing circumstances.
  • Two innovations: handle nested-data column style (column-striped representation) and multi-level execution trees
  • repetition levels (r) — at what repeated field in the field’s path the value has repeated.definition levels (d) — how many fields in path thatcould be undefined (because they are optional or repeated) are actually presentOnly repeated fields increment the repetition level, only non-required fields increment the definition level. Required fields are always defined and do not need a definition level. Non repeated fields do not need a repetition level.An optional field requires one extra bit to store zero if it is NULL and one if it is defined. NULL values do not need to be stored as the definition level captures this information.
  • Source query - Human (eg DSL) or tool written(eg SQL/ANSI compliant) query Source query is parsed and transformed to produce the logical planLogical plan: dataflow of what should logically be doneTypically, the logical plan lives in memory in the form of Java objects, but also has a textual formThe logical query is then transformed and optimized into the physical plan.Optimizer introduces of parallel computation, taking topology into accountOptimizer handles columnar data to improve processing speedThe physical plan represents the actual structure of computation as it is done by the systemHow physical and exchange operators should be appliedAssignment to particular nodes and cores + actual query execution per node
  • Drillbits per node, maximize data localityCo-ordination, query planning, optimization, scheduling, execution are distributedBy default, Drillbits hold all roles, modules can optionally be disabled.Any node/Drillbit can act as endpoint for particular query.
  • Zookeeper maintains ephemeral cluster membership information onlySmall distributed cache utilizing embedded Hazelcast maintains information about individual queue depth, cached query plans, metadata, locality information, etc.
  • Originating Drillbit acts as foreman, manages all execution for their particular query, scheduling based on priority, queue depth and locality information.Drillbit data communication is streaming and avoids any serialization/deserialization
  • Red: originating drillbit, is the root of the multi-level execution tree, per query/jobLeafs use their storage engine interface to scan respective data source (DB, file, etc.)
  • Handing over to Ted
  • Michael?
  • Transcript

    • 1. Large-scale, interactive ad-hoc queries over different datastores with Apache Drill Michael Hausenblas, Chief Data Engineer, MapR Technologies JAX London, 2013-10-29
    • 2. licensed under CC BY-NC-ND 2.0 Which workloads do you encounter in your environment?
    • 3. Batch processing Apache Pig Cascalog … for recurring tasks such as large-scale data mining, ETL offloading/data-warehousing  for the batch layer in Lambda architecture
    • 4. OLTP … user-facing eCommerce transactions, real-time messaging at scale (FB), time-series processing, etc.  for the serving layer in Lambda architecture
    • 5. Stream processing … in order to handle stream sources such as social media feeds or sensor data (mobile phones, RFID, weather stations, etc.)  for the speed layer in Lambda architecture
    • 6. Search/Information Retrieval … retrieval of items from unstructured documents (plain text, etc.), semi-structured data formats (JSON, etc.), as well as data stores (MongoDB, CouchDB, etc.)
    • 7. But what about interactive ad-hoc query at scale? licensed under CC BY-NC-ND 2.0
    • 8. Interactive Query (?) Impala low-latency
    • 9. Use Case: Marketing Campaign • Jane, a marketing analyst • Determine target segments • Data from different sources
    • 10. Use Case: Logistics • Supplier tracking and performance • Queries – Shipments from supplier ‘ACM’ in last 24h – Shipments in region ‘US’ not from ‘ACM’ SUPPLIER_ID NAME REGION ACM ACME Corp US GAL GotALot Inc US BAP Bits and Pieces Ltd Europe ZUP Zu Pli Asia { "shipment": 100123, "supplier": "ACM", “timestamp": "2013-02-01", "description": ”first delivery today” }, { "shipment": 100124, "supplier": "BAP", "timestamp": "2013-02-02", "description": "hope you enjoy it” } …
    • 11. Use Case: Crime Detection • • • • Online purchases Fraud, bilking, etc. Batch-generated overview Modes – Explorative – Alerts
    • 12. Requirements • • • • • Support for different data sources Support for different query interfaces Low-latency/real-time Ad-hoc queries Scalable, reliable
    • 13. And now for something completely different …
    • 14. Google’s Dremel “ Dremel is a scalable, interactive ad-hoc query system for analysis of read-only nested data. By combining multi-level execution trees and columnar data layout, it is capable of running aggregation queries over trillion-row tables in seconds. The system scales to thousands of CPUs and petabytes of data, and has thousands of users at Google. … “ Sergey Melnik, Andrey Gubarev, Jing Jing Long, Geoffrey Romer, Shiva Shivakumar, Matt Tolton, Theo Vassilakis, Proc. of the 36th Int'l Conf on Very Large Data Bases (2010), pp. 330339
    • 15. Google’s Dremel multi-level execution trees columnar data layout
    • 16. Google’s Dremel nested data + schema column-striped representation map nested data to tables
    • 17. Google’s Dremel experiments: datasets & query performance
    • 18. Back to Apache Drill …
    • 19. Apache Drill–key facts • • • • • • Inspired by Google’s Dremel Standard SQL 2003 support Plug-able data sources Nested data is a first-class citizen Schema is optional Community driven, open, 100’s involved
    • 20. High-level Architecture
    • 21. Principled Query Execution • Source query—what we want to do (analyst friendly) • Logical Plan— what we want to do (language agnostic, computer friendly) • Physical Plan—how we want to do it (the best way we can tell) • Execution Plan—where we want to do it
    • 22. Principled Query Execution Source Query SQL 2003 DrQL MongoQL DSL Parser parser API Logical Plan query: [ { @id: "log", op: "sequence", do: [ { op: "scan", source: “logs” }, { op: "filter", condition: "x > 3” }, Optimizer Topology CF etc. Physical Plan Execution scanner API
    • 23. Wire-level Architecture • Each node: Drillbit - maximize data locality • Co-ordination, query planning, execution, etc, are distributed • Any node can act as endpoint for a query—foreman Drillbit Drillbit Drillbit Drillbit Storage Process Storage Process Storage Process Storage Process node node node node
    • 24. Wire-level Architecture • Curator/Zookeeper for ephemeral cluster membership info • Distributed cache (Hazelcast) for metadata, locality information, etc. Curator/Zk Drillbit Drillbit Drillbit Drillbit Distributed Cache Distributed Cache Distributed Cache Distributed Cache Storage Process Storage Process Storage Process Storage Process node node node node
    • 25. Wire-level Architecture • Originating Drillbit acts as foreman: manages query execution, scheduling, locality information, etc. • Streaming data communication avoiding SerDe Curator/Zk Drillbit Drillbit Drillbit Drillbit Distributed Cache Distributed Cache Distributed Cache Distributed Cache Storage Process Storage Process Storage Process Storage Process node node node node
    • 26. Wire-level Architecture Foreman turns into root of the multi-level execution tree, leafs activate their storage engine interface. node Curator/Zk node node
    • 27. On the shoulders of giants … • • • • • • • • • • • • • Jackson for JSON SerDe for metadata Typesafe HOCON for configuration and module management Netty4 as core RPC engine, protobuf for communication Vanilla Java, Larray and Netty ByteBuf for off-heap large data structures Hazelcast for distributed cache Netflix Curator on top of Zookeeper for service registry Optiq for SQL parsing and cost optimization Parquet ( ORC Janino for expression compilation ASM for ByteCode manipulation Yammer Metrics for metrics Guava extensively Carrot HPC for primitive collections
    • 28. Key features • • • • Full SQL – ANSI SQL 2003 Nested Data as first class citizen Optional Schema Extensibility Points …
    • 29. Extensibility Points • • • • Source query  parser API Custom operators, UDF  logical plan Serving tree, CF, topology  physical plan/optimizer Data sources &formats  scanner API Source Query Parser Logical Plan Optimizer Physical Plan Execution
    • 30. User Interfaces • API—DrillClient – Encapsulates endpoint discovery – Supports logical and physical plan submission, query cancellation, query status – Supports streaming return results • JDBC driver, converting JDBC into DrillClient communication. • REST proxy for DrillClient
    • 31. User Interfaces
    • 33. Demo • Install • Preparation $ wget $ tar -zxf apache-drill-1.0.0-m1-binary-release.tar.gz $ export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.7.0_11.jdk/Contents/Ho me $ export DRILL_LOG_DIR=$PWD $ ./bin/ start
    • 34. Demo: submitting physical plan in a 3-node cluster Test 1: Scan JSON doc $ bin/submit_plan -f sample-data/physical_json_scan_test1.json -t physical -zk Test 2: Scan Parquet doc $ bin/submit_plan -f sample-data/parquet_scan_union_screen_physical.json -t physical -zk
    • 35. Demo: SQL on single node $ ./bin/sqlline -u jdbc:drill:schema=parquet-local 0: jdbc:drill:schema=parquet-local> SELECT _MAP['N_REGIONKEY'] as regionKey, _MAP['N_NAME'] as name FROM "sample-data/nation.parquet" WHERE cast(_MAP['N_NAME'] as varchar) < 'M';
    • 36. Demo: DIY
    • 37. Useful Resources • Getting Started guide • Demo HowTo mo+HowTo • How to build/install Apache Drill on Ubuntu 13.04
    • 38. BE A PART OF IT!
    • 39. Status • Heavy development by multiple organizations (MapR, Pentaho, Microsoft, Thoughtworks, XingCloud, etc.) • Currently more than 100k LOC • M1 Alpha available via
    • 40. Kudos to … • • • • • • • Julian Hyde, Pentaho Lisen Mu, XingCloud Tim Chen, Microsoft Chris Merrick, RJMetrics David Alves, UT Austin Sree Vaadi, SSS Srihari Srinivasan, ThoughtWorks • Alexandre Beche, CERN • Jason Altekruse, MapR • • • • • • • • • Ben Becker, MapR Jacques Nadeau, MapR Ted Dunning, MapR Keys Botzum, MapR Jason Frantz Ellen Friedman Chris Wensel, Concurrent Gera Shegalov, Oracle Ryan Rawson, Ohm Data
    • 41. Contributing Contributions appreciated—not only code drops … • Test data & test queries • Use case scenarios (textual/SQL queries) • Documentation
    • 42. Engage! • Follow @ApacheDrill on Twitter • Sign up at mailing lists (user | dev) • Standing G+ hangouts every Tuesday at 5pm GMT • Keep an eye on