Scott Leberknight Cloudera's 7/9/2013
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Scott Leberknight
Cloudera's
7/9/2013
History lesson...
Google Map/Reduce paper (2004)
Cutt ing & Cafare l la create Hadoop (2005)
Google Dremel paper (2010)
Facebook creates Hive (2007)*
Cloudera announces Impala (October 2012)
HortonWorks' Stinger (February 2013)
Apache Drill proposal (August 2012)
* Hive => "SQL on Hadoop"
Write SQL queries
Translate into Map/Reduce job(s)
Convenient & easy
High-latency (batch processing)
What is Impala?
In-memory, distributed SQL query engine (no Map/Reduce)
Native code (C++)
Distributed(on HDFS data nodes)
Why Impala?
Interactive data analysis
Low-latency response(roughly, 4 - 100x Hive)
Deploy on existing Hadoop clusters
Why Impala? (cont'd)
Data stored in HDFS avoids...
...duplicate storage
...data transformation
...moving data
Why Impala? (cont'd)
SPEED!
statestored & Hive metastore (for database metadata)
Overview
impalad daemon runs on HDFS nodes
Queries run on "relevant" nodes
Supports common HDFS file formats
(for cluster metadata)
Overview (cont'd)
Does not use Map/Reduce
Not fault tolerant ! (query fails if any query on any node fails)
Submit queries via Hue/Beeswax Thrift API, CLI, ODBC, JDBC
SQL Support
SELECT
Projection
UNION
INSERT OVERWRITE
INSERT INTO
ORDER BY(w/ LIMIT)
Aggregation
Subqueries(uncorrelated)
JOIN (equi-join only, subject to memory limitations)
(subset of Hive QL)
HBase Queries
Maps HBase tables via Hive metastore mapping
Row key predicates => start/stop row
Non-row key predicates => SingleColumnValueFilter
HBase scan translations:
(Very) Unscientific Benchmarks
9 queries, run in CDH Quickstart VM
Macbook Pro Retina, mid 201216GB RAM,4GB for VM (VMWare 5),Intel i7 2.6GHz quad-core processor
Hardware
No other load on system during queries
Pseudo-cluster + Impala daemons
CDH 4.2, Impala 1.0
Benchmarks (cont'd)
(from simple projection queries to multiple joins, aggregation, multiple
predicates, and order by)
Impala vs. Hive performance
"TPC-DS" sample dataset(http://www.tpc.org/tpcds/)
Query "A"
select c.c_first_name, c.c_last_namefrom customer c limit 50;
Query "B"
select c.c_first_name, c.c_last_name, ca.ca_city, ca.ca_county, ca.ca_state from customer c join customer_address ca on c.c_current_addr_sk = ca.ca_address_sklimit 50;
Query "C"
select c.c_first_name, c.c_last_name, ca.ca_city, ca.ca_county, ca.ca_statefrom customer c join customer_address ca on c.c_current_addr_sk = ca.ca_address_skwhere lower(c.c_last_name) like 'smi%'limit 50;
Query "D"
select distinct cd_credit_ratingfrom customer_demographics;
Query "E"
select cd_credit_rating, count(*)from customer_demographicsgroup by cd_credit_rating;
Query "F"select c.c_first_name, c.c_last_name, ca.ca_city, ca.ca_county, ca.ca_state, cd.cd_marital_status, cd.cd_education_statusfrom customer c join customer_address ca on c.c_current_addr_sk = ca.ca_address_sk join customer_demographics cd on c.c_current_cdemo_sk = cd.cd_demo_skwhere lower(c.c_last_name) like 'smi%' and cd.cd_credit_rating in ('Unknown', 'High Risk')limit 50;
Query "G"
select count(c.c_customer_sk)from customer c join customer_address ca on c.c_current_addr_sk = ca.ca_address_sk join customer_demographics cd on c.c_current_cdemo_sk = cd.cd_demo_skwhere ca.ca_zip in ('20191', '20194') and cd.cd_credit_rating in ('Unknown', 'High Risk');
Query "H"select c.c_first_name, c.c_last_name, ca.ca_city, ca.ca_county, ca.ca_state, cd.cd_marital_status, cd.cd_education_statusfrom customer c join customer_address ca on c.c_current_addr_sk = ca.ca_address_sk join customer_demographics cd on c.c_current_cdemo_sk = cd.cd_demo_skwhere ca.ca_zip in ('20191', '20194') and cd.cd_credit_rating in ('Unknown', 'High Risk')limit 100;
select i_item_id, s_state, avg(ss_quantity) agg1, avg(ss_list_price) agg2, avg(ss_coupon_amt) agg3, avg(ss_sales_price) agg4from store_salesjoin date_dim on (store_sales.ss_sold_date_sk = date_dim.d_date_sk)join item on (store_sales.ss_item_sk = item.i_item_sk)join customer_demographics on (store_sales.ss_cdemo_sk = customer_demographics.cd_demo_sk)join store on (store_sales.ss_store_sk = store.s_store_sk)where cd_gender = 'M' and cd_marital_status = 'S' and cd_education_status = 'College' and d_year = 2002 and s_state in ('TN','SD', 'SD', 'SD', 'SD', 'SD')group by i_item_id, s_stateorder by i_item_id, s_statelimit 100;
Query "TPC-DS"
Query Hive (sec) # M/R jobs Impala (sec) x Hive perf.
A 13.8 1 0.25 54
B 30.0 1 0.41 73
C 33.3 1 0.42 79
D 23.2 1 0.64 36
E 21.6 1 0.62 35
F 59.1 2 1.96 30
G 78.5 3 1.56 50
H 59.6 2 1.89 32
TPC-DS 204.5 6 3.23 63
(remember, unscientific...)
Arch
itect
ure
Two daemonsimpaladstatestored
impalad on each HDFS data node
statestored - cluster metadata
Thrift APIs, ODBC, JDBC
impalad
Query execution
Query coordination
Query planning
impalad
Query Coordinator
Query Planner
Query Executor
HDFS DataNode
HBase RegionServer
Queries performed in-memory
Intermediate data never hits disk!
Data streamed to clients
C++runtime code generationintrinsics for optimization
Execution engine:
statestored
Cluster membership
Acts as a cluster monitor
Not a SPOF(single point of failure)
Metadata
Impala uses Hive metastore
Daemons cache metadata
REFRESH when tabledefinition/data change
Create tables in Hive or Impala
Next up - how queries work...
impalad
Query Coordinator
Query Planner
Query Executor
HDFS DataNode
HBase RegionServer
Client Statestore Hive Metastore
table/database metadata
SQL query
impalad
Query Coordinator
Query Planner
Query Executor
HDFS DataNode
HBase RegionServer
impalad
Query Coordinator
Query Planner
Query Executor
HDFS DataNode
HBase RegionServer
clustermonitoring
Read directly from disk
Short-circuit reads
Bypass HDFS DataNode(avoids overhead of HDFS API)
impalad
Query Coordinator
Query Planner
Query Executor
HBase RegionServer
HDFS DataNode
Local Filesystem
Read directly
from disk
Current Limitations(as of version 1.0.1)
No join order optimization
No custom file formats, SerDes or UDFs
Limit required when using ORDER BY
Joins limited by aggregate memory of cluster
("put larger table on left")
Current Limitations(as of version 1.0.1)
No advanced data structures (arrays, maps, json, etc.)
Only basic DDL (otherwise do in Hive)
Limited file formats and compression(though probably fine for most people)
Future...
Structure types (structs, arrays, maps, json, etc.)
DDL support
Additional file formats & compression support
"Performance"
Join optimization(e.g. cost-based)
UDFs (???)
YARN integration
Fault-tolerance (???)
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.
Comparing Impala to Dremel
- http://research.google.com/pubs/pub36632.html
Comparing Impala to Dremel
Impala = Dremel features circa 2010 + join support, assuming columnar data format
(but, Google doesn't stand still...)
Dremel is production, mature
Basis for Google's BigQuery
Comparing Impala to Hive
Hive uses Map/Reduce -> high latency
Impala is in-memory, low-latency query engine
Impala sacrifices fault tolerance for performance
Comparing Impala to Drill
Apache Drill
Based on Dremel
In early stages...
"Apache Drill is an open-source software framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets. Drill is the open source version of Google's Dremel system which is available as an IaaS service called Google BigQuery. One explicitly stated design goal is that Drill is able to scale to 10,000 servers or more and to be able to process petabyes of data and trillions of records in seconds. Currently, Drill is incubating at Apache."
- http://incubator.apache.org/drill/drill_overview.html
Comparing Impala to Drill
"The Stinger Initiative is a collection of development threads in the Hive community that will deliver 100X performance improvements as well as SQL compatibility."
Comparing Impala to Stinger
- http://hortonworks.com/stinger/
Comparing Impala to Stinger
Stinger
Improve Hive performance (e.g. optimize execution plan)
Support for analytics (e.g. OVER clause, window functions)
TEZ framework to optimize execution
Columnar file format
http://hortonworks.com/stinger/
Stinger Phase 1 performance...
(Stinger phase 1 is really just Hive 0.11)
remember, these numbers are non-scientific micro-benchmarks!
Same 9 queries (as w/ Impala), run in HortonWorks Sandbox VM
Macbook Pro Retina, mid 201216GB RAM,4GB for VM (VMWare 5),Intel i7 2.6GHz quad-core processor
Hardware (same as w/ Impala)
No other load on system during queries
HortonWorks Data Platform (HDP) 1.3
Running pseudo-cluster
Query Hive (sec)# M/R jobs
StingerPhase 1 (sec)
# M/R jobs
x Hive perf.
A 13.8 1 10.0 1 1.4
B 30.0 1 15.8 1 1.9
C 33.3 1 14.1 1 2.4
D 23.2 1 18.7 1 1.2
E 21.6 1 19.7 1 1.1
F 59.1 2 34.3 1 1.7
G 78.5 3 35.2 1 2.2
H 59.6 2 31.5 1 1.9
TPC-DS 204.5 6 37.2 1 5.5
(remember, unscientific...)
QueryStinger Phase 1
(sec)Impala (sec) x Stinger perf.
A 10.0 0.25 39
B 15.8 0.41 38
C 14.1 0.42 33
D 18.7 0.64 29
E 19.7 0.62 32
F 34.3 1.96 18
G 35.2 1.56 23
H 31.5 1.89 17
TPC-DS 37.2 3.23 12
(remember, unscientific...)
Impala Review
In-memory, distributed SQL query engine
Integrates into existing HDFS
Not Map/Reduce
Focus on performance
(native code)
Competition...
Interactive data analysis
References
Google Dremel - http://research.google.com/pubs/pub36632.html
Apache Drill - http://incubator.apache.org/drill/
TPC-DS dataset - http://www.tpc.org/tpcds/
Stinger Initiative - http://hortonworks.com/blog/100x-faster-hive/ http://hortonworks.com/stinger/
Cloudera Impala resourceshttp://www.cloudera.com/content/support/en/documentation/cloudera-impala/cloudera-impala-documentation-v1-latest.html
Cloudera Impala: Real-Time Queries in Apache Hadoop, For Real
http://blog.cloudera.com/blog/2012/10/cloudera-impala-real-time-queries-in-apache-hadoop-for-real/
Photo Attributions
Impala - http://www.flickr.com/photos/gerardstolk/5897570970/
Measuring tape - http://www.morguefile.com/archive/display/24850
Bridge frame - http://www.morguefile.com/archive/display/9699
Balance - http://www.morguefile.com/archive/display/93433
* All others are iStockPhoto (I paid for them...)
My Info
twitter.com/sleberknight www.sleberknight.com/blog
scott dot leberknight at gmail dot com
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