Incredible Impala

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Impala:Modern, Open-Source SQL EngineFor HadoopGwen Shapira@gwenshapgshapira@cloudera.com

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Agenda

• Why Hadoop?• Data Processing in Hadoop• User’s view of Impala• Impala Use Cases• Impala Architecture• Performance highlights

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In the beginning….

was the database

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For a while, the database was all we needed.

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Data is not what it used to beD

ata

Gro

wth

STRUCTURED DATA – 20%

1980 2012

UNSTRUCTURED DATA – 80%

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Hadoop was Invented to Solve:

• Large volumes of data• Data that is only valuable in bulk• High ingestion rates• Data that requires more processing• Differently structured data• Evolving data• High license costs

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What is Apache Hadoop?

Has the Flexibility to Store and Mine Any Type of Data

Ask questions across structured and unstructured data that were previously impossible to ask or solve

Not bound by a single schema

Excels atProcessing Complex Data

Scale-out architecture divides workloads across multiple nodes

Flexible file system eliminates ETL bottlenecks

ScalesEconomically

Can be deployed on commodity hardware

Open source platform guards against vendor lock

Hadoop Distributed File System (HDFS)

Self-Healing, High Bandwidth Clustered

Storage

MapReduce

Distributed Computing Framework

Apache Hadoop is an open source platform for data storage and processing that is…

Distributed Fault tolerant Scalable

CORE HADOOP SYSTEM COMPONENTS

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Processing Data in Hadoop

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Map Reduce

• Versatile• Flexible• Scalable

• High latency• Batch oriented• Java• Challenging paradigm

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Hive & Pig

• Hive – Turn SQL into MapReduce• Pig – Turn execution plans into MapReduce• Makes MapReduce easier• But not any faster

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Towards a Better Map Reduce

• Spark – Next generation MapReduceWith in-memory cachingLazy EvaluationFast recovery times from node failures

• Tez – Next generation MapReduce. Reduced overhead, more flexibility.Currently Alpha

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And now to something completely different!

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What is Impala?

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Impala Overview

Interactive SQL for Hadoop Responses in seconds Nearly ANSI-92 standard SQL with Hive SQL

Native MPP Query Engine Purpose-built for low-latency queries Separate runtime from MapReduce Designed as part of the Hadoop ecosystem

Open Source Apache-licensed

Impala OverviewRuns directly within Hadoop

reads widely used Hadoop file formats talks to widely used Hadoop storage managers runs on same nodes that run Hadoop processes

High performance C++ instead of Java runtime code generation completely new execution engine – No MapReduce

Beta version released since October 2012 General availability (v1.0) release out since April 2013 Latest release (v1.2.3) released on December 23rd

Impala is Production Ready

User View of Impala: Overview

• Distributed service in cluster: one Impala daemon on each node with data

• Highly available: no single point of failure• Submit query to any daemon:

• ODBC/JDBC• Impala CLI• Hue

• Query is distributed to all nodes with relevant data• Impala uses Hive’s metadata

User View of Impala: File Formats

• There is no ‘Impala format’. • Impala supports:

• Uncompressed/lzo-compressed text files• Sequence files and RCFile with snappy/gzip

compression• Avro data files• Parquet columnar format (more on that later)• HBase

User View of Impala: SQL Support• Most of SQL-92• INSERT INTO … SELECT …• Only equi-joins; no non-equi joins, no cross products• Order By requires Limit (for now)• DDL support• SQL-style authorization via Apache Sentry (incubating)

• UDFs and UDAFs are supported

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Use Cases

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Impala Use Cases

Interactive BI/analytics on more data

Asking new questions – exploration, ML

Data processing with tight SLAs

Query-able archive w/full fidelity

Cost-effective, ad hoc query environment that offloads the data warehouse for:

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Global Financial Services Company

Saved 90% on incremental EDW spend &improved performance by 5x

Offload data warehouse for query-able archive

Store decades of data cost-effectively

Process & analyze on the same system

Improved capabilities through interactive query on more data

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Digital Media Company

20x performance improvement for exploration & data discovery

Easily identify new data sets for modeling

Interact with raw data directly to test hypotheses

Avoid expensive DW schema changes

Accelerate ‘time to answer’

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Impala Architecture

Impala Architecture

• Impala daemon (impalad) – N instances• Query execution

• State store daemon (statestored) – 1 instance• Provides name service and metadata distribution

• Catalog daemon (catalogd) – 1 instance• Relays metadata changes to all impalad’s

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Impala Query Execution

Query Planner

Query Coordinator

Query Executor

HDFS DN HBase

SQL App

ODBCHive

Metastore HDFS NN Statestore

Query Planner

Query Coordinator

Query Executor

HDFS DN HBase

Query Planner

Query Coordinator

Query Executor

HDFS DN HBase

SQL request

1) Request arrives via ODBC/JDBC/HUE/Shell

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Impala Query Execution

Query Planner

Query Coordinator

Query Executor

HDFS DN HBase

SQL App

ODBCHive

Metastore HDFS NN Statestore

Query Planner

Query Coordinator

Query Executor

HDFS DN HBase

Query Planner

Query Coordinator

Query Executor

HDFS DN HBase

2) Planner turns request into collections of plan fragments3) Coordinator initiates execution on impalad(s) local to data

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Impala Query Execution

Query Planner

Query Coordinator

Query Executor

HDFS DN HBase

SQL App

ODBCHive

Metastore HDFS NN Statestore

Query Planner

Query Coordinator

Query Executor

HDFS DN HBase

Query Planner

Query Coordinator

Query Executor

HDFS DN HBase

4) Intermediate results are streamed between impalad(s)5) Query results are streamed back to client

Query results

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Query Planner

2-phase planning Left deep tree Partition plan to maximize data locality

Join order Before 1.2.3: Order of tables in query. 1.2.3 and above: Cost based if statistics exist

Plan Operators Scan, HashJoin, HashAggregation, Union, TopN, Exchange All operators are fully distributed

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Query Execution Example

Simple Example

SELECT state, SUM(revenue)FROM HdfsTbl h JOIN HbaseTbl b ON (id)GROUP BY state ORDER BY 2 desc LIMIT 10

How does a database execute a query?

• Left Deep Tree• Data flows from bottom

to top TopN

Agg

HashJoin

HdfsScan

HbaseScan

Wait – Why is this a left-deep tree?

HashJoin

Scan: t1

Scan: t3

Scan: t2

HashJoin

Agg

HashJoin

Scan: t0

How does a database execute a query?

• Hash Join Node fills the hash table with the RHS table data.

• So, the RHS table (Hbase scan) is scanned first.

TopN

Agg

HashJoin

HdfsScan

HbaseScan

Scan Hbase

first

How does a database execute a query?

• Hash Join Node fills the hash table with the RHS table data. TopN

Agg

HashJoin

HdfsScan

HbaseScan

How does a database execute a query?

• Hash Join Node fills the hash table with the RHS table data. TopN

Agg

HashJoin

HdfsScan

HbaseScan

How does a database execute a query?

• Hash Join Node fills the hash table with the RHS table data. TopN

Agg

HashJoin

HdfsScan

HbaseScan

How does a database execute a query?

• Start scanning LHS (Hdfs) table

• For each row from LHS, probe the hash table for matching rows

TopN

Agg

HashJoin

HdfsScan

HbaseScan

Probe hash table and a matching row is found.

How does a database execute a query?

• Matched rows are bubbled up the execution tree TopN

Agg

HashJoin

HdfsScan

HbaseScan

How does a database execute a query?

• Continue scanning the LHS (Hdfs) table

• For each row from LHS, probe the hash table for matching rows

• Unmatched rows are discarded

TopN

Agg

HashJoin

HdfsScan

HbaseScan

No matching row

How does a database execute a query?

• Continue scanning the LHS (Hdfs) table

• For each row from LHS, probe the hash table for matching rows

• Unmatched rows are discarded

TopN

Agg

HashJoin

HdfsScan

HbaseScan

How does a database execute a query?

• Continue scanning the LHS (Hdfs) table

• For each row from LHS, probe the hash table for matching rows

• Unmatched rows are discarded

TopN

Agg

HashJoin

HdfsScan

HbaseScan

Probe hash table and a matching row is found.

How does a database execute a query?

• Matched rows are bubbled up the execution tree TopN

Agg

HashJoin

HdfsScan

HbaseScan

How does a database execute a query?

• Continue scanning the LHS (Hdfs) table

• For each row from LHS, probe the hash table for matching rows

• Unmatched rows are discarded

TopN

Agg

HashJoin

HdfsScan

HbaseScan

No matching row

How does a database execute a query?

• All rows have been returned from the hash join node. Agg node can start returning rows

• Rows are bubbled up the execution tree

TopN

Agg

HashJoin

HdfsScan

HbaseScan

How does a database execute a query?

• Rows from the aggregation node bubbles up to the top-n node

TopN

Agg

HashJoin

HdfsScan

HbaseScan

How does a database execute a query?

• Rows from the aggregation node bubbles up to the top-n node

• When all rows are returned by the agg node, top-n node can restart return rows to the end-user

TopN

Agg

HashJoin

HdfsScan

HbaseScan

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Key takeaways Data flows from bottom to top in the execution tree

and finally goes to the end user Larger tables go on the left Collect statistics Filter early

Simpler Example

SELECT state, SUM(revenue)FROM HdfsTbl h JOIN HbaseTbl b ON (id)GROUP BY state

How does an MPP database execute a query?

Tbl bScan

HashJoin

Tbl aScan

Exch

Agg

Exch

AggAgg

HashJoin

Tbl aScan

Tbl bScan

Broadcast

Re-distribute by “state”

How does a MPP database execute a query

A join B

A join B

A join B

Local Agg

Local Agg

Local Agg

Scan and Broadcast Tbl B

Final Agg

Final Agg

Final Agg

Re-distribute by “state”

Local read Tbl A

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Performance

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Impala Performance Results

• Impala’s Latest Milestone:• Comparable commercial MPP DBMS speed• Natively on Hadoop

• Three Result Sets:• Impala vs Hive 0.12 (Impala 6-70x faster)• Impala vs “DBMS-Y” (Impala average of 2x faster)• Impala scalability (Impala achieves linear scale)

• Background• 20 pre-selected, diverse TPC-DS queries (modified to remove unsupported

language)• Sufficient data scale for realistic comparison (3 TB, 15 TB, and 30 TB)• Realistic nodes (e.g. 8-core CPU, 96GB RAM, 12x2TB disks)• Methodical testing (multiple runs, reviewed fairness for competition, etc)

• Details: http://blog.cloudera.com/blog/2014/01/impala-performance-dbms-class-speed/

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Impala vs Hive 0.12 (Lower bars are better)

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Impala vs “DBMS-Y” (Lower bars are better)

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Impala Scalability: 2x the Hardware(Expectation: Cut Response Times in Half)

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Impala Scalability: 2x the Hardware and 2x Users/Data(Expectation: Constant Response Times)

2x the Users, 2x the Hardware

2x the Data, 2x the Hardware

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