• Save
HBaseCon 2013: Apache Drill - A Community-driven Initiative to Deliver ANSI SQL Capabilities for Apache HBase

Like this? Share it with your network

Share

HBaseCon 2013: Apache Drill - A Community-driven Initiative to Deliver ANSI SQL Capabilities for Apache HBase

  • 2,059 views
Uploaded on

Presented by: Jacques Nadeau, MapR

Presented by: Jacques Nadeau, MapR

More in: Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
2,059
On Slideshare
2,059
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
1
Comments
0
Likes
4

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. 1 Apache Drill: YASOH yet another sql on h(base|adoop) Jacques Nadeau, HBaseCon June 13, 2013 jacques@apache.org |@intjesus
  • 2. 2 Me  Software Architect @ MapR leading our Apache Drill contributions  Previously: – Lead development of distributed search engine at YapMap – Lead R&D team at contextual advertising company Quigo, sold to AOL – Built big data warehousing and analytical reporting products at Aquantive, sold to Microsoft
  • 3. 3 Apache Drill  Apache Incubating Project  Interactive Analysis of large scale datasets – Inspired by Google Dremel  MapReduce greatest strength is also an Achilles heel for high performance queries – Pessimistic execution is great for long running jobs – Optimistic execution is better for shorter jobs – Hive solves many needs but its organic growth and dependence on MapReduce make it hard to bring forward – Tez is a new project that tries to bring Hive a new execution model  Not Done—alpha next month
  • 4. 4 Basic Process Zookeeper DFS/HBase DFS/HBase DFS/HBase Drillbit Distributed Cache Drillbit Distributed Cache Drillbit Distributed Cache Query 1. Query comes to any Drillbit (JDBC, ODBC, CLI, protobuf) 2. Drillbit generates execution plan based on query optimization & locality 3. Fragments are farmed to individual nodes 4. Data is returned to driving node
  • 5. 5 Core Modules within a Drillbit SQL Parser Optimizer PhysicalPlan DFS Engine HBase Engine RPC Endpoint Distributed Cache StorageEngineInterface LogicalPlan Execution
  • 6. 6 SQL Options for HBase Drill Phoenix Impala Hive+Tez Overall Status Alpha 1.2 1.0 Alpha Typical Shortest Query 100ms 10ms 100ms ?? Query HBase ✓ ✓ ✓ ✓ Query Any SerDe ✓ ✓ Hive UDF support ✓ ✓ Contribution/Dev Model Apache GitHub MySQL Apache Execution programming language Java Java C++ Java Query language Supports Write ✓ ✓ ✓ Query Language SQL2003 SQL92 ~HiveQL HiveQL Data Supports data without schema ✓ Nested Relational Operators ✓ Internal sort & join ✓ ✓ ✓ External Sort/Join/Aggregation ✓ ✓ Execution Code Generation ✓ ✓ Columnar Execution ✓ Vectorized Operators ✓ ✓
  • 7. 7 What’s different about Drill  Late-bind schema doesn’t require metastore definitions SELECT cf1.month, cf1.year, FROM hbase.table1  Nested data as first class entity: Extensions to SQL for nested data types, similar to BigQuery (four-value semantics) SELECT c.name, c.address, COUNT(c.children) FROM( SELECT CONVERT_FROM(cf1.user-json-blob, JSON) AS c FROM hbase.table1 )
  • 8. 8 What’s different about Drill, cont’d  Community-driven Apache development process and peace of mind  Leverages recent research approaches – Late record materialization – Vectorized Operators  Extensibility – Supports Hive UDFs/SerDes – Well defined storage engine and operator interfaces – Logical and physical plan API layers for optimization and extension – Targeting Phoenix support  Works like other things in the Hadoop ecosystem – Apache development process & Java codebase
  • 9. 9 Drill + HBase Roadmap  Native support for Orderly complex keys – Orderly encodes a compound field (including null support) as a single, sortable byte value  Drill on top of Phoenix to leverage great Coprocessor work  Optimized HBase join leveraging bloomfilters  Memory mapped RegionServer <> Drillbit communication  Expression evaluation bytecode pushdown
  • 10. 10 Other Interesting Things  Drill keeps data off-heap to avoid garbage collection problems – Metadata stays on heap – Utilizes Netty’s arena-based NativeByteBuffer pooling and ByteBuf abstraction – RPC engine specifically designed to avoid extra memory copies – In memory representation is documented, allowing native operators as required  Code is compiled at a record batch level, avoiding record level function call overhead – Janino + ASM for code compilation – Recompiled for each schema change  Record batches are maintained in columnar format and leverage a selection vector execution method to speed query performance – Minimize branches and instruction complexity – Maximizes cache locality
  • 11. 11 Thanks!  Join the Community – Join the mailing list: • drill-dev-subscribe@incubator.apache.org • drill-user-subscribe@incubator.apache.org – Fork us on GitHub: http://github.com/apache/incubator-drill/ – Create a JIRA: https://issues.apache.org/jira/browse/DRI LL  Join the Drill team at MapR Technologies  Let us know what you think on the Drill mailing lists  Shout out to supporting projects – Jackson – Typesafe HOCON – Netty4 – Protobuf – Vanilla Java – Larray – Hazelcast – Curator – Optiq – Hive ORC – Parquet – Janino – ASM – Yammer Metrics – Guava – Carrot HPPC