A Survey of HBase Application Archetypes

Headline Goes Here
Speaker Name or Subhead Goes Here
DO NOT USE PUBLICLY
PRIOR TO 10/23/12
Apache HBase Application
Archetypes
Lars George | @larsgeorge | Cloudera EMEA Chief Architect | HBase PMC
Jonathan Hsieh | @jmhsieh | Cloudera HBase Tech lead | HBase PMC
HBaseCon 2014
May 5th , 2014
5/5/14 HBaseCon 2014; Lars George,
Jon Hsieh
1

About Lars and Jon
Lars George
• EMEA Chief Architect
@Cloudera
• Apache HBase PMC
• O’Reilly Author of HBase – The
Definitive Guide
• Contact
• lars@cloudera.com
• @larsgeorge
Jon Hsieh
• Tech Lead HBase Team
@Cloudera
• Apache HBase PMC
• Apache Flume founder
• Contact:
• jon@cloudera.com
• @jmhsieh
Jon Hsieh
2

About Supporting HBase at Cloudera
• Supporting Customers using HBase since 2011
• HBase Training
• Professional Services
• Team has experience supporting and running HBase since 2009
• 8 committers on staff
• 2 HBase book authors
• As of Jan 2014, ~20,000 HBase nodes (in aggregate) under
management
• Information in this presentation is either aggregated customer data
or from public sources.
Jon Hsieh
3

An Apache HBase Timeline
Jon Hsieh
20142008 2009 2010 2011 20132012
Apr’11: CDH3 GA
with HBase 0.90.1
May ‘12:
HBaseCon 2012
Jun ‘13:
HBaseCon 2013
Summer‘11:
Messages
on HBaseSummer ‘09
StumbleUpon
goes production on
HBase ~0.20
Nov ‘11:
Cassini
on HBase
Jan ‘13
Phoenix
on HBase
Summer‘11:
Web Crawl
Cache
4
Sept’11:
HBase TDG
published
Nov’12:
HBase in
Action
published
2015
May ‘14:
HBaseCon 2014
Aug ‘13
Flurry 1k-1k node
cluster replication
Summer ‘14
HBase v1.0.0
released
Jan’14: Cloudera
has ~20k Hbase
nodes under
management

Apache HBase “Nascar” Slide
Jon Hsieh
5

Outline
• Definitions
• Archetypes
• The Good
• The Bad
• The Maybe
• Conclusion
Jon Hsieh
6

A vocabulary for HBase Archetypes
Definitions
Jon Hsieh
7

Defining HBase Archetypes
• There are a lot of HBase applications
• Some successful, some less so
• They have common architecture patterns
• They have common tradeoffs
• Archetypes are common architecture patterns
• Common across multiple use-cases
• Extracted to be repeatable
• Our Goal: Define patterns à la “Gang of Four” (Gamma, Helm,
Johnson, Vlissides)
Jon Hsieh
8

So you want to use HBase?
• What data is being stored?
• Entity data
• Event data
• Why is the data being stored?
• Operational use cases
• Analytical use cases
• How does the data get in and out?
• Real time vs. Batch
• Random vs. Sequential
Jon Hsieh
9

What is being stored?
There are primarly two kinds of big data workloads. They have
different storage requirements.
Entities Events
Jon Hsieh
10

Entity Centric Data
• Entity data is information about current state
• Generally real time reads and writes
• Examples:
• Accounts
• Users
• Geolocation points
• Click Counts and Metrics
• Current Sensors Reading
• Scales up with # of Humans and # of Machines/Sensors
• Billions of distinct entities
Jon Hsieh
11

Event Centric Data
• Event centric data are time-series data points recording successive points
spaced over time intervals.
• Generally real time write, some combination of real time read or batch read
• Examples:
• Sensor data over time
• Historical Stock Ticker data
• Historical Metrics
• Clicks time-series
• Scales up due to finer grained intervals, retention policies, and the passage
of time
Jon Hsieh
12

Events about Entities
• Majority Big Data use cases are dealing with event-based data
• |Entities| * |Events| = Big data
• When you ask questions, do you hone in on entity first?
• When you ask questions, do you hone in on time ranges first?
• Your answer will help you determine where and how to store
your data.
Jon Hsieh
13

Why are you storing the data?
• So what kind of questions are you asking the data?
• Entity-centric questions
• Give me everything about entity e
• Give me the most recent event v about entity e
• Give me the n most recent events V about entity e
• Give me all events V about e between time [t1,t2]
• Event and Time-centric questions
• Give me an aggregates on each entity between time [t1,t2]
• Give me an aggregate on each time interval for entity e
• Find events V that match some other given criteria
Jon Hsieh
14

How does data get in and out of HBase?
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner
Bulk Import
HBase Client
15
HBase
Replication
HBase
Replication

How does data get in and out of HBase?
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Get, Scan
Bulk Import
HBase Client
16
HBase
Replication
HBase
Replication
low latency
high throughput
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner

What system is most efficient?
• It is all physics
• You have a limited I/O budget
• Use all your I/O by parallelizing access
and read/write sequentially.
• Choose the system and features that
reduces I/O in general
• Pick the systems best for your workload
Jon Hsieh
17
IOPs/s/disk

The physics of Hadoop Storage Systems
Workload HBase HDFS
Low latency ms, cached mins, MR
+ seconds, Impala
Random Read primary index - index?, small files problem
Short Scan sorted + partition
Full Scan 0 live table
+ (MR on snapshots)
MR, Hive, Impala
Random Write log structured - Not supported
Sequential Write hbase overhead
bulk load
minimal overhead
Updates log structured - Not supported
Jon Hsieh
18

Workload HBase HDFS
+ seconds, Impala
+ (MR on snapshots)
MR, Hive, Impala
Random Write log structured - Not supported
Sequential Write hbase overhead
bulk load
minimal overhead
Updates log structured - Not supported
Jon Hsieh
19

Workload HBase HDFS
+ seconds, Impala
+ (MR on snapshots)
MR, Hive, Impala
Random Write log structured - not supported
Sequential Write HBase overhead
bulk load
minimal overhead
Updates log structured - not supported
Jon Hsieh
20

The Archetypes
HBase Applications
Jon Hsieh
21

HBase application use cases
• The Good
• Simple Entities
• Messaging Store
• Graph Store
• Metrics Store
• The Bad
• Large Blobs
• Naïve RDBMS port
• Analytic Archive
• The Maybe
• Time series DB
• Combined workloads
Jon Hsieh
22

Archetypes: The Good
HBase, you are my soul mate.
Jon Hsieh
23

Archetype: Simple Entities
• Purely entity data, no relation between entities
• Batch or real-time, random writes
• Real-time, random reads
• Could be a well-done denormalized RDBMS port.
• Often from many different sources, with poly-structured data
• Schema:
• Row per entity
• Row key => entity ID, or hash of entity ID
• Col qualifier => Property / field, possibly time stamp
• Geolocation data
• Search index building
• Use solr to make text data searchable.
Jon Hsieh
24

Simple Entities access pattern
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Get, Scan
Bulk Import
HBase Client
25
HBase
Replication
low latency
high throughput
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner
HBase
Replication
Solr

Archetype: Messaging Store
• Messaging Data:
• Realtime Random writes: Emails, SMS, MMS, IM
• Realtime random updates: Msg read, starred, moved, deleted
• Reading of top-N entries, sorted by time
• Records are of varying size
• Some time series, but mostly random read/write
• Schema:
• Row = users/feed/inbox
• Row key = UID or UID + time
• Column Qualifier = time or conversation id + time.
• Use CF’s for indexes.
• Examples:
• Facebook Messages, Xiaomi Messages
• Telco SMS/MMS services
• Feeds like tumblr, pinterest
Jon Hsieh
26

Facebook Messages - Statistics
Source: HBaseCon 2012 - Anshuman Singh
Jon Hsieh
27

Messages Access Pattern
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Get, Scan
Bulk Import
HBase Client
28
HBase
Replication
HBase
Replication
low latency
high throughput
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner

Archetype: Graph Data
• Graph Data: All entities and relations
• Batch or realtime, random writes
• Batch or realtime, random reads
• Its an entity with relation edges
• Schema:
• Row = Node.
• Row key => Node ID.
• Col qualifier => Edge ID, or properties:values
• Examples:
• Web Caches – Yahoo!, Trend Micro
• Titan Graph DB with HBase storage backend
• Sessionization (financial transactions, clicks streams, network traffic)
• Government (connect the bad guy)
Jon Hsieh
29

Graph Data Access Pattern
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Get, Scan
Bulk Import
HBase Client
30
HBase
Replication
HBase
Replication
low latency
high throughput
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner

Archetype: Metrics
• Frequently updated Metrics
• Increments
• Roll ups generated by MR and bulk loaded to HBase
• Poor man’s datacubes
• Examples
• Campaign Impression/Click counts (Ad tech)
• Sensor data (Energy, Manufacturing, Auto)
Jon Hsieh
31

Metrics Access Pattern
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Get, Scan
Bulk Import
HBase Client
32
HBase
Replication
HBase
Replication
low latency
high throughput
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner

CONFIDENTIAL - RESTRICTED
Archetypes: The Bad
These are not the droids you are looking for
33

Current HBase weak spots
• HBase’s architecture can handle a lot
• We make engineering trade offs to optimize for them.
• HBase can still do things it is not optimal for.
• However, other systems are fundamentally more efficient for some
workloads.
• We’ve often seen some folks forcing apps into HBase.
• If one of these is your only workloads on this data, use another system
• If you are in a mixed workload case, some of these become “maybes”.
• Just because it is not good today, doesn’t mean it cant be better
tomorrow.
Jon Hsieh
34

Bad Archetype: Large Blob Store
• Saving large objects >3MB per cell
• Schema:
• Normal entity pattern, but with some columns with large cells.
• Examples
• Raw photo or video storage in HBase
• Large frequently updated structs as a single cell
• Problems:
• Will get crushed due to write amplification when reoptimizing data for read.
(compactions on large unchanging data)
• Will crush write pipeline if there are large structs with frequently updated subfields.
Cells are atomic, and hbase must rewrite an entire cell.
• Some work adding LOB support
• This requires new architecture elements
Jon Hsieh
35

Bad Archetype: Naïve RDBMS port
• A naïve port the RDBMS onto HBase, directly copying the schema.
• Schema
• Many tables, just like an RDBMS schema.
• Row key: primary key or auto-incrementing key, like RDBMS schema
• Column qualifiers: field names
• Manually do joins, or secondary indexes (not consistent)
• Solution:
• HBase is not a SQL Database.
• No multi-region/multi-table in HBase transactions (yet).
• Must to denormalize your schema to use Hbase.
Jon Hsieh
36

Large blob store, Naïve RDBMS port access patterns
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Get, Scan
Bulk Import
HBase Client
37
HBase
Replication
HBase
Replication
low latency
high throughput
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner

Bad Archetype: Analytic archive
• Store purely chronological data, partitioned by time
• Real time writes, chronological time as primary index
• Column-centric aggregations over all rows.
• Bulk reads out, generally for generating periodic reports
• Schema
• Row key: date+xxx or salt+date+xxx
• Column qualifiers: properties with data or counters
• Example
• Machine logs organized by date.
• Full fidelity clickstream
Jon Hsieh
38

Bad Archetype: Analytic archive Problems
• HBase non-optimal as primary use case.
• Will get crushed by frequent full table scans.
• Will get crushed by large compactions.
• Will get crushed by write-side region hot spotting.
• Instead
• Store in HDFS; Use Parquet columnar data storage + Impala/Hive
• Build rollups in HDFS+MR; store and serve rollups in HBase
Jon Hsieh
39

Analytic Archive access patterns
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Get, Scan
Bulk Import
HBase Client
40
HBase
Replication
HBase
Replication
low latency
high throughput
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner

And this is crazy | But here’s my data, | serve it, maybe!
Archetypes: The Maybe
Jon Hsieh
41

The Maybe’s
• For some applications, doing it right gets complicated.
• These more sophisticated or nuanced cases require considing
these questions:
• When do you choose HBase vs HDFS storage for time series data?
• Are there times where bad archetypes are ok?
Jon Hsieh
42

Time Series: in HBase or HDFS?
• IO Patterns:
• Reads: Collocate related data
• Make reads cheap and fast.
• Writes: Spread writes out as much as possible
• Maximize write throughput
• HBase: Tension between these goals
• Spreading writes spreads data making reads inefficient
• Colocating on write causes hotspots, underutilizes resources by limiting write
throughput
• HDFS: The sweet spot.
• Sequential writes and and sequential read.
• Just write more files in date-dirs; physically spreads writes but logically groups data.
• Reads for time centric quieries just read files in date-dir
Jon Hsieh
43

Time Series data flows
• Ingest
• Flume or similar direct tool via app
• HDFS
• Batch queries and generate rollups in Hive/MR
• Faster queries in Impala
• No user time serving
• HBase for recent, HDFS for historical
• HBase
• Serve individual events
• Serve pre-computed aggregates
Jon Hsieh
44

Archetype: Entity Time Series
• A time series access pattern suitable for HBase
• Random write to event data, random read specific event or aggregate data
• Generate aggregates via counters, don’t directly compute aggregate on
query
• HBase is system of record
• Schema:
• Rowkey: entity-timestamp or hash(entity)-timestamp, possibly with salt
added after entity.
• Col qualifiers: property
• Use custom aggretation to consolidate old data
• Use TTL’s to bound and age off old data
• Examples:
• OpenTSDB does this well for numeric values; Lazily aggregates cells for
better performance.
• Facebook Insights, ODS
Jon Hsieh
45

Entity Time Series access pattern
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Get, Scan
Bulk Import
HBase Client
46
HBase
Replication
HBase
Replication
low latency
high throughput
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner
Flume
Custom App

Archetypes: Hybrid Entity Time Series
• Essentially a combo of the Metric Archetype and Entity Time
Series Archetype, with bulk loads of rollups via HDFS.
• Land data in HDFS and HBase
• Keep all data in HDFS for future use
• Aggregate in HDFS and write to HBase
• HBase can do some aggregates too (counters)
• Keep serve-able data in HBase.
• Use TTL to discard old values from Hbase.
Jon Hsieh
47

Hybrid time series access pattern
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Get, Scan
Hive or MR:
Bulk Import
HBase Client
48
HBase
Replication
HBase
Replication
low latency
high throughput
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner
HDFS
Flume

Meta Archetype: Combined workloads
• In these cases, the use of HBase depends on workload
• Cases where we have multiple workloads styles.
• Many cases we want to do multiple things with the same
data
• primary use case (real time, random access)
• secondary use case (analytical)
• Pick for your primary, here’s some patterns on how to do
your secondary.
Jon Hsieh
49

Real time workloads and Analytical access
Jon Hsieh
HBase Client
Get, Scan
50
poor latency!
full scans
interfere with
latency!
high throughput
MapReduce
HBase Scanner
HBase Client
Put, Incr,
Append
Bulk Import
HBase Client
HBase
Replication

Real time workloads and Analytical access
Jon Hsieh
HBase Client
Get, Scan
51
HBase
Replication
low latency
Isolated from full scans
high throughput
MapReduce
HBase Scanner
HBase Client
Put, Incr,
Append
Bulk Import
HBase Client
HBase
Replication
high throughput

MR over Table Snapshots (0.98, CDH5.0)
• Previously MapReduce jobs over
HBase required online full table
scan
• Take a snapshot and run MR job
over snapshot files
• Doesn’t use HBase client
• Avoid affecting HBase caches
• 3-5x perf boost.
• Still requires more IOPs than hdfs
raw files
Jon Hsieh
map
map
map
map
map
map
map
map
reduce
reduce
reduce
map
map
map
map
map
map
map
map
reduce
reduce
reduce
snapshot
52

Analytic Archive access pattern
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Get, Scan
Bulk Import
HBase Client
53
HBase
Replication
HBase
Replication
low latency
high throughput
Gets
Short scan
Full Scan,
MapReduce
HBase Scanner

Analytic Archive Snapshot access pattern
HDFS
HBase Client
Put, Incr,
Append
Jon Hsieh
HBase Client
Snapshot
Scan, MR
HBase Scanner
Bulk Import
HBase Client
54
HBase
Replication
HBase
Replication
low latency
Higher throughput
Table snapshot
Gets
Short scan

Multitenancy (in progress)
• We want to MR for analytics while
serving low-latency requests in one
cluster.
• Performance Isolation
• Limit performance impact load on
one table has on others. (HBASE-
6721)
• Request prioritization and scheduling
• Toda default is FIFO
• Need to schedule some requests
before others (HBASE-10994)
Jon Hsieh
55
1 1 2 1 1 3 1
1 1 21 1 31
Delayed by long
scan requests
Rescheduled so
new request get
priority
Mixed workload
Isolated
workload

Conclusions
Jon Hsieh
56

Big Data Workloads
Jon Hsieh
57
Low
latency
Batch
Random Access Full ScanShort Scan
HDFS + MR
(Hive/pig)
HBase
HBase + Snapshots
-> HDFS + MR
HDFS
+ Impala
HBase + MR

Big Data Workloads
Jon Hsieh
58
Low
latency
Batch
Random Access Full ScanShort Scan
HDFS + MR
(Hive/pig)
HBase
HBase + Snapshots
-> HDFS + MR
HDFS
+ Impala
HBase + MR
Current Metrics
Graph data
Simple Entities
Hybrid Entity Time series
+ Rollup serving
Messages
Analytic archive
Hybrid Entity Time series
+ Rollup generation
Index building
Entity Time series

HBase is evolving to be an Operational Database
• Excels at consistent single row centric operations
• Dev efforts aimed at using all machine resources efficiently,
reducing MTTR, and improving latency predictability.
• Projects built on HBase that enable secondary indexing and
multi-row transactions
• Apache Phoenix (incubating) or Impala provide a SQL skin for
simplified application development
• Analytic workloads?
• Can be done but will be beaten by direct HDFS +
MR/Spark/Impala
Jon Hsieh
59

Questions?
@larsgeorge
@jmhsieh
Jon Hsieh
60

A Survey of HBase Application Archetypes

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