HBase Storage Internals
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Apache HBase is the Hadoop opensource, distributed, versioned storage manager well suited for random, realtime read/write access. This talk will give an overview on how HBase achieve random I/O, focusing on the storage layer internals. Starting from how the client interact with Region Servers and Master to go into WAL, MemStore, Compactions and on-disk format details. Looking at how the storage is used by features like snapshots, and how it can be improved to gain flexibility, performance and space efficiency.
![What
is
HBase
• Open
source
Storage
Manager
that
provides
random
read/write
on
top
of
HDFS
• Provides
Tables
with
a
“Key:Column/Value”
interface
• Dynamic
columns
(qualifiers),
no
schema
needed
• “Fixed”
column
groups
(families)
• table[row:family:column]
=
value
2](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-2-320.jpg)
![HBase
EcoSystem
• Apache
Hadoop
HDFS
for
data
durability
and
reliability
(Write-‐Ahead
App
MR
Log)
• Apache
ZooKeeper
for
distributed
coordina]on
ZK
HDFS
• Apache
Hadoop
MapReduce
built-‐in
support
for
running
MapReduce
jobs
3](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-3-320.jpg)
![Master,
Region
Servers
and
Regions
• Region
Server
Client
• Server
that
contains
a
set
of
Regions
ZooKeeper
• Responsible
to
handle
reads
and
writes
• Region
Master
• The
basic
unit
of
scalability
in
HBase
• Subset
of
the
table’s
data
Region
Server
Region
Server
Region
Server
• Con]guous,
sorted
range
of
rows
stored
Region
Region
Region
together.
Region
Region
Region
• Master
Region
Region
Region
• Coordinates
the
HBase
Cluster
HDFS
• Assignment/Balancing
of
the
Regions
• Handles
admin
opera]ons
• create/delete/modify
table,
…
5](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-5-320.jpg)
![Autosharding
and
.META.
table
• A
Region
is
a
Subset
of
the
table’s
data
• When
there
is
too
much
data
in
a
Region…
• a
split
is
triggered,
crea]ng
2
regions
• The
associa]on
“Region
-‐>
Server”
is
stored
in
a
System
Table
• The
Loca]on
of
.META.
Is
stored
in
ZooKeeper
Table
Start
Key
Region
ID
Region
Server
machine01
Region
1
-‐
testTable
testTable
Key-‐00
1
machine01.host
Region
4
-‐
testTable
testTable
Key-‐31
2
machine03.host
machine02
testTable
Key-‐65
3
machine02.host
Region
3
-‐
testTable
testTable
Key-‐83
4
machine01.host
Region
1
-‐
users
…
…
…
…
machine03
users
Key-‐AB
1
machine03.host
Region
2
-‐
testTable
users
Key-‐KG
2
machine02.host
Region
2
-‐
users
6](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-6-320.jpg)
![The
Write
Path
–
Create
a
New
Table
• The
client
asks
to
the
master
to
create
a
new
Table
• hbase>
create
‘myTable’,
‘cf’
Client
createTable()
• The
Master
Master
• Store
the
Table
informa]on
(“schema”)
Store
Table
“Metadata”
• Create
Regions
based
on
the
key-‐splits
provided
Assign
the
Regions
“enable”
• no
splits
provided,
one
single
region
by
Region
Region
Region
default
Server
Region
Server
Server
Region
Region
• Assign
the
Regions
to
the
Region
Servers
Region
Region
Region
• The
assignment
Region
-‐>
Server
is
wri6en
to
a
system
table
called
“.META.”
7](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-7-320.jpg)
![The
Write
Path
–
“Inser]ng”
data
Client
• table.put(row-‐key:family:column,
value)
Where
is
.META.?
Scan
.META.
• The
client
asks
ZooKeeper
the
loca]on
of
.META.
ZooKeeper
Region
Server
Insert
Region
• The
client
scans
.META.
searching
for
the
KeyValue
Region
Region
Server
responsible
to
handle
the
Key
Region
Server
Region
• The
client
asks
the
Region
Server
to
Region
insert/update/delete
the
specified
key/value.
Region
• The
Region
Server
process
the
request
and
dispatch
it
to
the
Region
responsible
to
handle
the
Key
• The
opera]on
is
wri6en
to
a
Write-‐Ahead
Log
(WAL)
• …and
the
KeyValues
added
to
the
Store:
“MemStore”
8](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-8-320.jpg)
![The
Write
Path
–
Append
Only
to
Random
R/W
• Files
in
HDFS
are
RS
Region
WAL
Region
Region
• Append-‐Only
• Immutable
once
closed
MemStore
+
Store
Files
(HFiles)
• HBase
provides
Random
Writes?
• …not
really
from
a
storage
point
of
view
• KeyValues
are
stored
in
memory
and
wri6en
to
disk
on
pressure
• Don’t
worry
your
data
is
safe
in
the
WAL!
Key0
–
value
0
• (The
Region
Server
can
recover
data
from
the
WAL
is
case
of
crash)
Key1
–
value
1
Key2
–
value
2
Key3
–
value
3
But
this
allow
to
sort
data
by
Key
before
wri]ng
on
disk
•
Key4
–
value
4
Key5
–
value
5
• Deletes
are
like
Inserts
but
with
a
“remove
me
flag”
Store
Files
9](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-9-320.jpg)
![The
Read
Path
–
“reading”
data
• The
client
asks
ZooKeeper
the
loca]on
of
.META.
Client
Where
is
• The
client
scans
.META.
searching
for
the
Region
Server
.META.?
Scan
.META.
responsible
to
handle
the
Key
ZooKeeper
Region
Server
Region
• The
client
asks
the
Region
Server
to
get
the
specified
key/
Get
Key
Region
value.
Region
Server
• The
Region
Server
process
the
request
and
dispatch
it
to
Region
Region
the
Region
responsible
to
handle
the
Key
Region
• MemStore
and
Store
Files
are
scanned
to
find
the
key
10](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-10-320.jpg)
![The
Read
Path
–
Append
Only
to
Random
R/W
• Each
flush
a
new
file
is
created
Key0
–
value
0.0
Key0
–
value
0.1
Key2
–
value
2.0
Key5
–
value
5.0
Key3
–
value
3.0
Key1
–
value
1.0
Key5
–
value
5.0
Key5
–
[deleted]
Key8
–
value
8.0
Key6
–
value
6.0
• Each
file
have
KeyValues
sorted
by
key
Key9
–
value
9.0
Key7–
value
7.0
• Two
or
more
files
can
contains
the
same
key
(updates/deletes)
• To
find
a
Key
you
need
to
scan
all
the
files
• …with
some
op]miza]ons
• Filter
Files
Start/End
Key
• Having
a
bloom
filter
on
each
file
11](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-11-320.jpg)
![HFile
format
Blocks
• Only
Sequen]al
Writes,
just
append(key,
value)
Header
• Large
Sequen]al
Reads
are
be6er
Record
0
Record
1
• Why
grouping
records
in
blocks?
Key/Value
…
(record)
Record
N
• Easy
to
split
Key
Length
:
int
Header
Value
Length
:
int
Record
0
• Easy
to
read
Key
:
byte[]
Record
1
…
• Easy
to
cache
Value
:
byte[]
Record
N
Index
0
• Easy
to
index
(if
records
are
sorted)
…
Index
N
• Block
Compression
(snappy,
lz4,
gz,
…)
Trailer
13](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-13-320.jpg)
![Data
Block
Encoding
• “Be
aware
of
the
data”
• Block
Encoding
allows
to
compress
the
Key
based
on
what
we
know
• Keys
are
sorted…
prefix
may
be
similar
in
most
cases
• One
file
contains
keys
from
one
Family
only
• Timestamps
are
“similar”,
we
can
store
the
diff
“on-‐disk”
• Type
is
“put”
most
of
the
]me…
KeyValue
Row
Length
:
short
Row
:
byte[]
Family
Length
:
byte
Family
:
byte[]
Qualifier
:
byte[]
Timestamp
:
long
Type
:
byte
14](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-14-320.jpg)
![Compac]ons
Op]mize
the
read-‐path
15](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-15-320.jpg)
![Compac]ons
• Reduce
the
number
of
files
to
look
into
during
a
scan
Key0
–
value
0.0
Key0
–
value
0.1
Key2
–
value
2.0
Key1
–
value
1.0
Key3
–
value
3.0
Key4–
value
4.0
Key5
–
value
5.0
Key5
–
[deleted]
Key8
–
value
8.0
Key6
–
value
6.0
• Removing
duplicated
keys
(updated
values)
Key9
–
value
9.0
Key7–
value
7.0
• Removing
deleted
keys
Key0
–
value
0.1
Key1
–
value
1.0
• Creates
a
new
file
by
merging
the
content
of
2+
files
Key2
–
value
2.0
Key4–
value
4.0
Key6
–
value
6.0
Key7–
value
7.0
Key8–
value
8.0
• Remove
the
old
files
Key9–
value
9.0
16](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-16-320.jpg)
![Pluggable
Compac]ons
• Try
different
algorithm
Key0
–
value
0.0
Key0
–
value
0.1
Key2
–
value
2.0
Key1
–
value
1.0
Key3
–
value
3.0
Key4–
value
4.0
Key5
–
value
5.0
Key5
–
[deleted]
Key8
–
value
8.0
Key6
–
value
6.0
• Be
aware
of
the
data
Key9
–
value
9.0
Key7–
value
7.0
• Time
Series?
I
guess
no
updates
from
the
80s
Key0
–
value
0.1
• Be
aware
of
the
requests
Key1
–
value
1.0
Key2
–
value
2.0
Key4–
value
4.0
Key6
–
value
6.0
Key7–
value
7.0
• Compact
based
on
sta]s]cs
Key8–
value
8.0
Key9–
value
9.0
• which
files
are
hot
and
which
are
not
• which
keys
are
hot
and
which
are
not
17](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-17-320.jpg)
![How
taking
a
snapshot
works?
• The
master
orchestrate
the
RSs
• the
communica]on
is
done
via
ZooKeeper
• using
a
“2-‐phase
commit
like”
transac]on
(prepare/commit)
• Each
RS
is
responsible
to
take
its
“piece”
of
snapshot
• For
each
Region
store
the
metadata
informa]on
needed
• (list
of
Store
Files,
WALs,
region
start/end
keys,
…)
ZK
ZK
Master
ZK
RS
RS
Region
WAL
Region
Region
Region
WAL
Region
Region
Store
Files
(HFiles)
Store
Files
(HFiles)
19](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-19-320.jpg)
![What
is
a
Snapshots?
• “a
Snapshot
is
not
a
copy
of
the
table”
• a
Snapshot
is
a
set
of
metadata
informa]on
• The
table
“schema”
(column
families
and
a6ributes)
• The
Regions
informa]on
(start
key,
end
key,
…)
• The
list
of
Store
Files
• The
list
of
WALs
ac]ve
ZK
ZK
Master
ZK
RS
RS
Region
WAL
Region
Region
Region
WAL
Region
Region
Store
Files
(HFiles)
Store
Files
(HFiles)
20](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-20-320.jpg)
![Compac]ons
&
Archiving
• HFiles
are
immutable,
and
shared
between
tables
and
snapshots
• On
compac]on
or
table
dele]on,
files
are
removed
from
disk
• If
files
are
referenced
by
a
snapshot
or
a
cloned
table
• The
file
is
moved
to
an
“archive”
directory
• And
deleted
later,
when
there’re
no
references
to
it
22](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-22-320.jpg)
![Compac]ons
Op]mize
the
read-‐path
23](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-23-320.jpg)
![0.96
is
coming
up
• Moving
RPC
to
Protobuf
• Allows
rolling
upgrades
with
no
surprises
• HBase
Snapshots
• Pluggable
Compac]ons
• Remove
-‐ROOT-‐
• Table
Locks
24](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-24-320.jpg)
![0.98
and
Beyond
• Transparent
Table/Column-‐Family
Encryp]on
• Cell-‐level
security
• Mul]ple
WALs
per
Region
Server
(MTTR)
• Data
Placement
Awareness
(MTTR)
• Data
Type
Awareness
• Compac]on
policies,
based
on
the
data
needs
• Managing
blocks
directly
(instead
of
files)
25](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-25-320.jpg)
![Ques]ons?
26](https://image.slidesharecdn.com/bertozzihadoopsummitcloudera-pptx-130328175538-phpapp01/85/HBase-Storage-Internals-26-320.jpg)
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HBase Storage Internals
- 1. HBase Storage Internals, present and future! Ma6eo Bertozzi | @Cloudera March 2013 -‐ Hadoop Summit Europe 1
- 2. What is HBase • Open source Storage Manager that provides random read/write on top of HDFS • Provides Tables with a “Key:Column/Value” interface • Dynamic columns (qualifiers), no schema needed • “Fixed” column groups (families) • table[row:family:column] = value 2
- 3. HBase EcoSystem • Apache Hadoop HDFS for data durability and reliability (Write-‐Ahead App MR Log) • Apache ZooKeeper for distributed coordina]on ZK HDFS • Apache Hadoop MapReduce built-‐in support for running MapReduce jobs 3
- 4. How HBase Works? “View from 10000c” 4
- 5. Master, Region Servers and Regions • Region Server Client • Server that contains a set of Regions ZooKeeper • Responsible to handle reads and writes • Region Master • The basic unit of scalability in HBase • Subset of the table’s data Region Server Region Server Region Server • Con]guous, sorted range of rows stored Region Region Region together. Region Region Region • Master Region Region Region • Coordinates the HBase Cluster HDFS • Assignment/Balancing of the Regions • Handles admin opera]ons • create/delete/modify table, … 5
- 6. Autosharding and .META. table • A Region is a Subset of the table’s data • When there is too much data in a Region… • a split is triggered, crea]ng 2 regions • The associa]on “Region -‐> Server” is stored in a System Table • The Loca]on of .META. Is stored in ZooKeeper Table Start Key Region ID Region Server machine01 Region 1 -‐ testTable testTable Key-‐00 1 machine01.host Region 4 -‐ testTable testTable Key-‐31 2 machine03.host machine02 testTable Key-‐65 3 machine02.host Region 3 -‐ testTable testTable Key-‐83 4 machine01.host Region 1 -‐ users … … … … machine03 users Key-‐AB 1 machine03.host Region 2 -‐ testTable users Key-‐KG 2 machine02.host Region 2 -‐ users 6
- 7. The Write Path – Create a New Table • The client asks to the master to create a new Table • hbase> create ‘myTable’, ‘cf’ Client createTable() • The Master Master • Store the Table informa]on (“schema”) Store Table “Metadata” • Create Regions based on the key-‐splits provided Assign the Regions “enable” • no splits provided, one single region by Region Region Region default Server Region Server Server Region Region • Assign the Regions to the Region Servers Region Region Region • The assignment Region -‐> Server is wri6en to a system table called “.META.” 7
- 8. The Write Path – “Inser]ng” data Client • table.put(row-‐key:family:column, value) Where is .META.? Scan .META. • The client asks ZooKeeper the loca]on of .META. ZooKeeper Region Server Insert Region • The client scans .META. searching for the KeyValue Region Region Server responsible to handle the Key Region Server Region • The client asks the Region Server to Region insert/update/delete the specified key/value. Region • The Region Server process the request and dispatch it to the Region responsible to handle the Key • The opera]on is wri6en to a Write-‐Ahead Log (WAL) • …and the KeyValues added to the Store: “MemStore” 8
- 9. The Write Path – Append Only to Random R/W • Files in HDFS are RS Region WAL Region Region • Append-‐Only • Immutable once closed MemStore + Store Files (HFiles) • HBase provides Random Writes? • …not really from a storage point of view • KeyValues are stored in memory and wri6en to disk on pressure • Don’t worry your data is safe in the WAL! Key0 – value 0 • (The Region Server can recover data from the WAL is case of crash) Key1 – value 1 Key2 – value 2 Key3 – value 3 But this allow to sort data by Key before wri]ng on disk • Key4 – value 4 Key5 – value 5 • Deletes are like Inserts but with a “remove me flag” Store Files 9
- 10. The Read Path – “reading” data • The client asks ZooKeeper the loca]on of .META. Client Where is • The client scans .META. searching for the Region Server .META.? Scan .META. responsible to handle the Key ZooKeeper Region Server Region • The client asks the Region Server to get the specified key/ Get Key Region value. Region Server • The Region Server process the request and dispatch it to Region Region the Region responsible to handle the Key Region • MemStore and Store Files are scanned to find the key 10
- 11. The Read Path – Append Only to Random R/W • Each flush a new file is created Key0 – value 0.0 Key0 – value 0.1 Key2 – value 2.0 Key5 – value 5.0 Key3 – value 3.0 Key1 – value 1.0 Key5 – value 5.0 Key5 – [deleted] Key8 – value 8.0 Key6 – value 6.0 • Each file have KeyValues sorted by key Key9 – value 9.0 Key7– value 7.0 • Two or more files can contains the same key (updates/deletes) • To find a Key you need to scan all the files • …with some op]miza]ons • Filter Files Start/End Key • Having a bloom filter on each file 11
- 12. HFile HBase Store File Format 12
- 13. HFile format Blocks • Only Sequen]al Writes, just append(key, value) Header • Large Sequen]al Reads are be6er Record 0 Record 1 • Why grouping records in blocks? Key/Value … (record) Record N • Easy to split Key Length : int Header Value Length : int Record 0 • Easy to read Key : byte[] Record 1 … • Easy to cache Value : byte[] Record N Index 0 • Easy to index (if records are sorted) … Index N • Block Compression (snappy, lz4, gz, …) Trailer 13
- 14. Data Block Encoding • “Be aware of the data” • Block Encoding allows to compress the Key based on what we know • Keys are sorted… prefix may be similar in most cases • One file contains keys from one Family only • Timestamps are “similar”, we can store the diff “on-‐disk” • Type is “put” most of the ]me… KeyValue Row Length : short Row : byte[] Family Length : byte Family : byte[] Qualifier : byte[] Timestamp : long Type : byte 14
- 15. Compac]ons Op]mize the read-‐path 15
- 16. Compac]ons • Reduce the number of files to look into during a scan Key0 – value 0.0 Key0 – value 0.1 Key2 – value 2.0 Key1 – value 1.0 Key3 – value 3.0 Key4– value 4.0 Key5 – value 5.0 Key5 – [deleted] Key8 – value 8.0 Key6 – value 6.0 • Removing duplicated keys (updated values) Key9 – value 9.0 Key7– value 7.0 • Removing deleted keys Key0 – value 0.1 Key1 – value 1.0 • Creates a new file by merging the content of 2+ files Key2 – value 2.0 Key4– value 4.0 Key6 – value 6.0 Key7– value 7.0 Key8– value 8.0 • Remove the old files Key9– value 9.0 16
- 17. Pluggable Compac]ons • Try different algorithm Key0 – value 0.0 Key0 – value 0.1 Key2 – value 2.0 Key1 – value 1.0 Key3 – value 3.0 Key4– value 4.0 Key5 – value 5.0 Key5 – [deleted] Key8 – value 8.0 Key6 – value 6.0 • Be aware of the data Key9 – value 9.0 Key7– value 7.0 • Time Series? I guess no updates from the 80s Key0 – value 0.1 • Be aware of the requests Key1 – value 1.0 Key2 – value 2.0 Key4– value 4.0 Key6 – value 6.0 Key7– value 7.0 • Compact based on sta]s]cs Key8– value 8.0 Key9– value 9.0 • which files are hot and which are not • which keys are hot and which are not 17
- 18. Snapshots Zero-‐copy snapshots and table clones 18
- 19. How taking a snapshot works? • The master orchestrate the RSs • the communica]on is done via ZooKeeper • using a “2-‐phase commit like” transac]on (prepare/commit) • Each RS is responsible to take its “piece” of snapshot • For each Region store the metadata informa]on needed • (list of Store Files, WALs, region start/end keys, …) ZK ZK Master ZK RS RS Region WAL Region Region Region WAL Region Region Store Files (HFiles) Store Files (HFiles) 19
- 20. What is a Snapshots? • “a Snapshot is not a copy of the table” • a Snapshot is a set of metadata informa]on • The table “schema” (column families and a6ributes) • The Regions informa]on (start key, end key, …) • The list of Store Files • The list of WALs ac]ve ZK ZK Master ZK RS RS Region WAL Region Region Region WAL Region Region Store Files (HFiles) Store Files (HFiles) 20
- 21. Cloning a Table from a Snapshots • hbase> clone_snapshot ‘snapshotName’, ‘tableName’ … • Creates a new table with the data “contained” in the snapshot • No data copies involved • HFiles are immutable • And shared between tables and snapshots • You can insert/update/remove data from the new table • No repercussions on the snapshot, original tables or other cloned tables 21
- 22. Compac]ons & Archiving • HFiles are immutable, and shared between tables and snapshots • On compac]on or table dele]on, files are removed from disk • If files are referenced by a snapshot or a cloned table • The file is moved to an “archive” directory • And deleted later, when there’re no references to it 22
- 23. Compac]ons Op]mize the read-‐path 23
- 24. 0.96 is coming up • Moving RPC to Protobuf • Allows rolling upgrades with no surprises • HBase Snapshots • Pluggable Compac]ons • Remove -‐ROOT-‐ • Table Locks 24
- 25. 0.98 and Beyond • Transparent Table/Column-‐Family Encryp]on • Cell-‐level security • Mul]ple WALs per Region Server (MTTR) • Data Placement Awareness (MTTR) • Data Type Awareness • Compac]on policies, based on the data needs • Managing blocks directly (instead of files) 25
- 26. Ques]ons? 26
- 27. Thank you! Ma6eo Bertozz, @cloudera @th30z