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Apache Hive 2.0: SQL, Speed, ScaleAlan GatesCo-founder HortonworksApril 2016
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Acknowledgements
The Apache Hive community for building all this awesome tech Content of some of these slides based on earlier presentations by Sergey Shelukhin
and Siddarth Seth alias Hive=‘Apache Hive’
alias Hadoop=‘Apache Hadoop’alias Spark=‘Apache Spark’alias Tez=‘Apache Tez’alias Parquet=‘Apache Parquet’alias ORC=‘Apache ORC’alias Omid=‘Apache Omid (incubating)’alias Calcite=‘Apache Calcite’
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Hive 1.x and 2.x
New feature development in Hive moving at a fast pace– Stressful for those who use Hive for its original purpose (ETL type SQL on MapReduce)– Realizing the full potential of Hive as data warehouse on Hadoop requires more changes
Compromise: follow Hadoop’s example, split into stable and new feature lines 1.x
– Stable– Backwards compatible– Ongoing bug fixes
2.x– Major new features– Backwards compatible where possible, but some things will be broken– Hive 2.0 released February 15, 2016 – Not considered production ready
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Hive 2.0 New Features Overview
1039 JIRAs resolved with 2.0 as fix version– 666 bugs– 140 improvements or new features
HPLSQL LLAP HBase Metastore Hive-On-Spark Improvements Cost Based Optimizer Improvements Many, many new features and bug fixes I will not have time to cover
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Adding Procedural SQL: HPLSQL
Procedural SQL, akin to Oracle’s PL/SQL and Teradata’s stored procedures– Adds cursors, loops (FOR, WHILE, LOOP), branches (IF), HPLSQL procedures, exceptions (SIGNAL)
Aims to be compatible with all major dialects of procedural SQL to maximize re-use of existing scripts
Currently external to Hive, communicates with Hive via JDBC. – User runs command using hplsql binary– Goal is to tightly integrate it so that Hive’s parser can execute HPLSQL, store HPLSQL procedures,
etc.
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Sub-second Queries in Hive: LLAP (Live Long and Process)
Persistent daemons– Saves time on process start up (eliminates container allocation and JVM start up time)– All code JITed within a query or two
Data caching with an async I/O elevator– Hot data cached in memory (columnar aware, so only hot columns cached)– When possible work scheduled on node with data cached, if not work will be run in other node
Operators can be executed inside LLAP when it makes sense– Large, ETL style queries usually don’t make sense– User code not run in LLAP for security
Working on interface to allow other data engines to read securely in parallel Beta in 2.0
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Hive With LLAP Execution Options
AM AM
T T T
R R
R
T T
T
R
M M M
R R
R
M M
R
R
Tez Only LLAP + Tez
T T T
R R
R
T T
T
R
LLAP only
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LLAP Performance
query3 query12 query20 query21 query26 query27 query42 query52 query55 query73 query89 query91 query980
5
10
15
20
25
30
35
40
45
50
LLAP vs Hive 1.x 10TB Scale
LLAP Hive 1.x
Tim
e (s
econ
ds)
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LLAP Performance Continued
query3
query12
query17
query21
query26
query28
query43
query48
query55
query65
query73
query82
query89
query98
query18
query25
query31
query34
query40
query49
query51
query56
query66
query71
query79
query85
query88
query92
query94
query96
0
50
100
150
200
250
300
350
400
450
500
LLAP Hive 1.2.1
Tim
e (s
econ
ds)
Hive / LLAP, Hive 1.2.1 Query Times
38 out of 61 queries ran 50% faster 25 out of 61 queries ran 70% faster12 out of 61 queries ran 80% faster1 query ran 90% faster
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LLAP Limitations
Currently in Beta Read only, no write path yet Does not work with ACID yet (see previous bullet) User must decide whether query runs fully in LLAP, mixed mode, or not at all
– Should be handled by CBO
Currently only reads ORC files Currently only integrates with Tez as an engine
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Speeding up Query Planning: HBase Metastore
Add option to use HBase to store Hive’s metadata Why?
– Planning a query that reads several thousand partitions in Hive 1.2 takes 5+ seconds, mostly for metadata acquisition– ORM layer produces complex, slow schema (40+ tables)– The need to work across 5 different databases limits performance optimizations and maximizes test matrix for
developers– Limits caching opportunities as we cannot store too much data in a single node RDBMS– The need to limit number of concurrent connections forces all metadata operations to be done during query
planning– HBase addresses each of these
Goal: cut metadata access time for query with thousands of partitions to 200 milliseconds– Not there yet, currently at 1-1.5 seconds
Challenges– HBase lacks transactions, addressing via Apache Omid (incubating)
Alpha in Hive 2.0
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Improvements to Hive on Spark
Dynamic partition pruning Make use of spark persistence for self-join, self-union, and CTEs Vectorized map-join and other map-join improvements Parallel order by Pre-warming of containers Support for Spark 1.5 Many bug fixes
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Cost Base Optimizer (CBO) Improvements
Hive’s CBO uses Calcite– Not all optimization rules migrated yet, but 2.0 continues work towards that
CBO on by default in 2.0 (wasn’t in in 1.x) Main focus of CBO work has been BI queries (using TPC-DS as guide)
– Some work on machine generated queries, since tools generate some funky queries
Focus on improving stats collection and estimating stats more accurately between operators in the plan
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And Many, Many More
• SQL Standard Auth is the default authorization (actually works)• CLI mode for beeline (WIP to replace and deprecate CLI in Hive 2.*)• Codahale-based metrics (also in 1.3)• HS2 Web UI• Stability Improvements and bugfixes for ACID (almost production ready now)• Native vectorized mapjoin, vectorized reducesink, improved vectorized GBY, etc.• Improvements to Parquet performance (PPD, memory manager, etc.)• ORC schema evolution (beta)• Improvement to windowing functions, refactoring ORC before split, SIMD
optimizations, new LIMIT syntax, parallel compilation in HS2, improvements to Tez session management, many more
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Hive 2.0 Incompabilities
Java 7 & 8 supported, 6 no longer supported Requires Hadoop 2.x, Hadoop 1.x no longer supported MapReduce deprecated, Tez or Spark recommended instead
– At some future date MR will be removed
Some configuration defaults changed, e.g.– bucketing enforced by default– metadata schema no longer created if it is missing– SQL Standard authorization used by default
We plan to remove Hive CLI in the future and replace with beeline CLI– Why?
• Makes it easier for users to deploy secure clusters where all access is via [OJ]DBC• It is cleaner to maintain one code path
– Does not require HiveServer2, can run HS2 embedded in beeline
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Thank You