2. Hadoop for Financial Services
First completely Hadoop-powered analytics
application
Widely recognized as “Big Data Startup to
Watch”
Winner of the 2011 NCTA Award for Emerging
Tech Company of the Year
Based in Charlotte NC
We are hiring! curious@tresata.com
3. Software Development Lead at Tresata
Background in Financial Services IT
End-User Applications
Data Warehousing
ETL
Email: chris@tresata.com
Twitter: @chrisshain
4. What is HBase?
From: http://hbase.apache.org:
▪ “HBase is the Hadoop database.”
I think this is a confusing statement
5. ‘Database’, to many, means:
Transactions
Joins
Indexes
HBase has none of these
More on this later
6. HBase is a data storage platform designed to
work hand-in-hand with Hadoop
Distributed
Failure-tolerant
Semi-structured
Low latency
Strictly consistent
HDFS-Aware
“NoSQL”
7. Need for a low-latency, distributed datastore
with unlimited horizontal scale
Hadoop (MapReduce) doesn’t provide low-
latency
Traditional RDBMS don’t scale out
horizontally
8. November 2006: Google BigTable whitepaper
published:
http://research.google.com/archive/bigtable.html
February 2007: Initial HBase Prototype
October 2007: First ‘usable’ HBase
January 2008: HBase becomes Apache
subproject of Hadoop
March 2009: HBase 0.20.0
May 10th, 2010: HBase becomes Apache Top
Level Project
9. Web Indexing
Social Graph
Messaging (Email etc.)
10. HBase is written almost entirely in Java
JVM clients are first-class citizens
HBase Master
RegionServer
JVM Clients
RegionServer
Non-JVM Proxy
RegionServer
Clients (Thrift or REST)
RegionServer
11. All data is stored in Tables
Table rows have exactly one Key, and all rows in a
table are physically ordered by key
Tables have a fixed number of Column Families
(more on this later!)
Each row can have many Columns in each column
family
Each column has a set of values, each with a
timestamp
Each row:family:column:timestamp combination
represents coordinates for a Cell
12. Defined by the Table
A Column Family is a group of related
columns with it’s own name
All columns must be in a column family
Each row can have a completely different set
of columns for a column family
Row: Column Family: Columns:
Chris Friends:Bob
Bob Friends Friends:Chris Friends:James
James Friends:Bob
13. Not exactly the same as rows in a traditional
RDBMS
Key: a byte array (usually a UTF-8 String)
Data: Cells, qualified by column family, column, and
timestamp (not shown here)
Row Key: Column Families : Columns: Cells:
(Defined by the (Defined by the Row) (Created with Columns)
Table) (May vary between
rows)
Attributes Attributes:Age 30
Attributes:Height 68
Chris
Friends Friends:Bob 1 (Bob’s a cool guy)
Friends:Jane 0 (Jane and I don’t get along)
14. All cells are created with a timestamp
Column family defines how many versions of
a cell to keep
Updates always create a new cell
Deletes create a tombstone (more on that
later)
Queries can include an “as-of” timestamp to
return point-in-time values
15. HBase deletes are a form of write called a
“tombstone”
Indicates that “beyond this point any previously
written value is dead”
Old values can still be read using point-in-time
queries
Timestamp Write Type Resulting Value Point-In-Time
Value “as of” T+1
T+0 PUT (“Foo”) “Foo” “Foo”
T+1 PUT (“Bar”) “Bar” “Bar”
T+2 DELETE <none> “Bar”
T+3 PUT (“Foo Too”) “Foo Too” “Bar”
16. Requirement: Store real-time stock tick data
Ticker Timestamp Sequence Bid Ask
IBM 09:15:03:001 1 179.16 179.18
MSFT 09:15:04:112 2 28.25 28.27
GOOG 09:15:04:114 3 624.94 624.99
IBM 09:15:04:155 4 179.18 179.19
Requirement: Accommodate many
simultaneous readers & writers
Requirement: Allow for reading of current
price for any ticker at any point in time
18. Row Key Family:Column
Prices:Bid
[Ticker].[Rev_Timestamp].[Rev_Sequence_Number]
Prices:Ask
HBase throughput will scale linearly with # of
nodes
No need to keep separate “latest price” table
A scan starting at “ticker” will always
return the latest price row
19. HBase scales horizontally
Needs to split data over many RegionServers
Regions are the unit of scale
20. All HBase tables are broken into 1 or more
regions
Regions have a start row key and an end row
key
Each Region lives on exactly one
RegionServer
RegionServers may host many Regions
When RegionServers die, Master detects this
and assigns Regions to other RegionServers
21. “Users” Table
Row Keys in Region
“Aaron” – “George”
-META- Table “Aaron”
Region “Bob”
Table Region
Server “Chris”
“Aaron” – “George” Node01 Row Keys in Region
Users “George” – “Matthew” Node02 “George” – “Matthew”
“Matthew” – “Zachary” Node01 “George”
Row Keys in Region
“Matthew” – “Zachary”
“Matthew”
“Nancy”
“Zachary”
24. ZooKeeper
Keeps track of which server is the current HBase
Master
HBase Master
Keeps track of Region/RegionServer mapping
Manages the -ROOT- and .META. tables
Responsible for updating ZooKeeper when these
change
25. RegionServer
Stores table regions
Clients
Need to be smarter than RDBMS clients
First connect to ZooKeeper to get RegionServer
for a given Table/Region
Then connect directly to RegionServer to interact
with the data
All connections over Hadoop RPC – non-JVM
clients use proxy (Thrift or REST (Stargate))
27. HBase Master is not necessarily a single point of
failure (SPOF)
Multiple masters can be running
Current ‘active’ Master controlled via ZooKeeper
Make sure you have enough ZooKeeper nodes!
Master is not needed for client connectivity
Clients connect directly to ZooKeeper to find Regions
Everything Master does can be put off until one is
elected
29. HBase tolerates RegionServer failure when
running on HDFS
Data is replicated by HDFS (dfs.replication setting)
Lots of issues around fsync, failure before data is
flushed - some probably still not fixed
Thus, data can still be lost if node fails after a write
HDFS NameNode is still SPOF, even for HBase
30. Similar to log in many RDBMS
All operations by default written to log before considered
‘committed’ (can be overridden for ‘disposable fast writes’)
Log can be replayed when region is moved to another
RegionServer
One WAL per RegionServer
Flushed periodically
(10s by default)
WAL HFile
Writes
MemStore HFile
Flushed when
MemStore gets too big
32. A RegionServer is not guaranteed to be on
the same physical node as it’s data
Compaction causes RegionServer to write
preferentially to local node
But this is a function of HDFS Client, not HBase
33. All data is in memory initially (memstore)
HBase is a write-only system
Modifications and deletes are just writes with
later timestamps
Function of HDFS being append-only
Eventually old writes need to be discarded
2 Types of Compactions:
Minor
Major
34. All HBase edits are initially stored in memory
(memstore)
Flushes occur when memstore reaches a
certain size
By default 67,108,864 bytes
Controlled by hbase.hregion.memstore.flush.size
configuration property
Each flush creates a new HFile
35. Triggered when a certain number of HFiles are created for
a given Region Store (+ some other conditions)
By default 3 HFiles
Controlled by hbase.hstore.compactionThreshold configuration
property
Compacts most recent HFiles into one
By default, uses RegionServer-local HDFS node
Does not eliminate deletes
Only touches most recent HFiles
NOTE: All column families are compacted at once (this
might change in the future)
36. Triggered every 24 hours (with random
offset) or manually
Large HBase installations usually leave this for
manual operators
Re-writes all HFiles into one
Processes deletes
Eliminates tombstones
Erases earlier entries
37. HBase does not have transactions
However:
Row-level modifications are atomic: All
modifications to a row will succeed or fail as a unit
Gets are consistent for a given point in time
▪ But Scans may return 2 rows from different points in
time
All data read has been ‘durably stored’
▪ Does NOT mean flushed to disk- can still be lost!
38.
39. DO: Design your schema for linear range scans on your
most common queries.
Scans are the most efficient way to query a lot of rows
quickly
DON’T: Use more than 2 or 3 column families.
Some operations (flushing and compacting) operate
on the whole row
DO: Be aware of the relative cardinality of column
families
Wildly differing cardinality leads to sparsity and bad
scanning results.
40. DO: Be mindful of the size of your row and column
keys
They are used in indexes and queries, can be quite
large!
DON’T: Use monotonically increasing row keys
Can lead to hotspots on writes
DO: Store timestamp keys in reverse
Rows in a table need to be read in order, usually
you want most recent
41. DO: Query single rows using exact-match on key
(Gets) or Scans for multiple rows
Scans allow efficient I/O vs. multiple gets
DON’T: Use regex-based or non-prefix column filters
Very inefficient
DO: Tune the scan cache and batch size parameters
Drastically improves performance when returning lots of
rows
45. Requirement: Store an arbitrary set of
preferences for all users
Requirement: Each user may choose to store
a different set of preferences
Requirement: Preferences may be of
different data types (Strings, Integers, etc)
Requirement: Developers will add new
preference options all the time, so we
shouldn’t need to modify the database
structure when adding them
46. One possible RDBMS solution:
Key/Value table
All values as strings
Flexible, but wastes space
Keys: Column: Data Type:
UserID Int
PK
PreferenceName Varchar
PreferenceValue Varchar
47. Store all preferences in the Preferences
column family
Preference name as column name,
preference value as (serialized) byte array:
HBase client library provides methods for
serializing many common data types
Row Key: Family: Column: Value:
Age 30
Chris Preferences
Hometown “Mineola, NY”
Joe Preferences Birthdate 11/13/1987
