introduction to hbase - phoenix hug 5/14

© 2014 MapR Technologies 1© 2014 MapR Technologies

Introduction to Apache HBase

Jeremy Walsh – Solutions Architect, MapR – [email protected]

5/7/2014

mailto:[email protected]

© 2014 MapR Technologies 2

Agenda

• HBase Overview• HBase APIs• MapR Tables• Example• Securing tables


What’s HBase??

• A NoSQL database– Synonym for ‘non-traditional’ database

• A distributed columnar data store– Storage layout implies performance characteristics

• The “Hadoop” database• A semi-structured database

– No rigid requirements to define columns or even data types in advance– It’s all bytes to HBase

• A persistent sorted Map of Maps– Programmers view


Relational database model vs. NoSQL

• RDBMS: MySQL, Oracle, MS SQL Server, DB2, Postgres…

• Non-relational database models used with Big Data– Key-value: Riak, Redis– Column-oriented : MapR Tables, HBase, Cassandra– Document-oriented : MongoDB, CouchDB– Graph: Neo4J, OrientDB


Relational Model• RDBMS (Relational Database Management

System) – Standard persistence model– Data is normalized, split into tables when stored

• typed and structured before stored

– Joined back together when read• Structured Query Language

• Pros– Many business rules map well to a tabular structure

and relationships• Layout of the data is known in advance

– Transactions handle concurrency , consistency – Provides an efficient and robust structure for

storing data


Column Oriented

• Row is indexed by a key – Data stored sorted by key

• Data is stored by columns grouped into column families– Each family is a file of column values laid out in sorted order by row key– Contrast this to a traditional row oriented database where rows are stored together

with fixed space allocated for each row

CF1

colA colB colC

val val

val

CF2

colA colB colC

val val

val

Customer Address data Customer order dataCustomer id

RowKey

axxx

gxxx


HBase is…

• Distributed column-oriented database built on top of HDFS/MapR-FS.

• Open-source implementation of Google’s Big Table– Semi-structured data– Commodity Hardware– Horizontal Scalability– Part of Hadoop system, and integrated with MapReduce

• Is the Hadoop application to use when you require real-time read/write random access to very large datasets.

• Provides fault-tolerant way of storing large quantities of sparse data


ZooKeeperZooKeeper

Region Server Data Node




What is HBase? (Cluster View)

• ZooKeeper (ZK)

• HMaster (HM)

• Region Servers (RS)

For MapR, there is less delineation between Control and Data Nodes.

A

HMaster

C DHMaster Masterservers

Slaveservers





NameNode

A B

ZooKeeper


What is a Region?• The basic partitioning/sharding unit of HBase.• Each region is assigned a range of keys it is responsible for.• Region servers serve data for reads and writes

ZooKeeperZooKeeperZooKeeper

HMaster

Region

Container

Key colB

colC

val val

val

Region

Key colB

colC

val val

val

Region

Container

Key colB

colC

val val

val

Region

Key colB

colC

val val

val

Client


HBase Data Model- Row Keys

• Row Keys: identify the rows in an HBase table.

RowKey

CF1 CF2 …

colA colB colC colA colB colC colD

R1axxx val val val val…

gxxx val val val val

R2hxxx val val val val val val val…

jxxx val

R3kxxx val val val val…

rxxx val val val val val val

… sxxx val val


Rows are Stored in Sorted Order

• Sorting of row key is based upon binary values– Sort is lexicographic at byte level– Comparison is “left to right”

• Example: – Sort order for String 1, 2, 3, …, 99, 100:

1, 10, 100, 11, 12,…, 2, 20, 21, …, 9, 91, 92, …, 98, 99– Sort order for String 001, 002, 003, …, 099, 100:

001, 002, 003, …, 099, 100– What if the RowKeys were numbers converted to fixed

sized binary?


Tables are split into Regions = contiguous keys

• Tables are partitioned into key ranges (regions)• Region= contiguous keys, served by nodes (RegionServers)• Regions are spread across cluster: S1, S2…

Source: Diagram from Lars George’s HBase: The Definitive Guide.

Key RangeRegion1

Key Range

axxx

gxxx

Region 2Key Range

Lxxx

zxxx

Region

CF1

colA colB colC

val val

val

CF2

colA colB colC

val val

val

Region

Row key

axxx

gxxx

Region Server for Region 2, 3


HBase Data Model- Cells

• Value for each cell is specified by complete coordinates:– RowKey Column Family Column Version: Value– Key:CF:Col:Version:Value

RowKey CF:Qualifier

version value

smithj Data:street 12734567800

Main street

Column Key


Sparsely-Populated Data

• Missing values: Cells remain empty and consume no storage

RowKey

CF1 CF2 …

colA colB colC colA colB colC colD

R1axxx val val val val…

gxxx val val val val

R2hxxx val val val val val val val…

jxxx val

R3kxxx val val val val…

rxxx val val val val val val

… sxxx val val


HBase Data Model Summary• Efficient/Flexible

– Storage allocated for columns only as needed on a given row• Great for sparse data• Great for data of widely varying size

– Adding columns can be done at any time without impact

– Compression and versioning are usually built-in and take advantage of column family storage (like data together)

• Highly Scalable– Data is sharded amongst regions based upon key

• Regions are distributed in cluster

– Grouping by key = related data stored together

• Finding data– Key implies region and server, column family implies file– Efficiently get to any data by key


Agenda



Basic Table Operations

• Create Table, define Column Families before data is imported– But not the rows keys or number/names of columns

• Basic data access operations (CRUD):

put Inserts data into rows (both add and update)get Accesses data from one rowscan Accesses data from a range of rowsdelete Delete a row or a range of rows or columns


CRUD Operations Follow A Pattern (mostly)

• Most common pattern– Instantiate object for an operation: Put put = new Put(key)– Add or Set attributes to specify what you need: put.add(…)– Execute the operation against the table: myTable.put(put)

// Insert value1 into rowKey in columnFamily:columnName1

Put put = new Put(rowKey);

put.add(columnFamily, columnName1, value1);

myTable.put(put);

// Retrieve values from rowA in columnFamily:columnName1

Get get = new Get(rowKey);

get.addColumn(columnFamily, columnName1);

Result result = myTable.get(get);


Put Example

byte [] invTable = Bytes.toBytes("/path/Inventory"); byte [] stockCF = Bytes.toBytes(“stock"); byte [] quantityCol = Bytes.toBytes (“quantity”); long amt = 24l; HTableInterface table = new HTable(hbaseConfig, invTable);

Put put = new Put(Bytes.toBytes (“pens”));

put.add(stockCF, quantityCol, Bytes.toBytes(amt));

table.put(put);

CF “stock”

quantity

pens 24

Inventory


Put Operation – Add method• Once a Put instance is created you call an add method on it • Typically you add a value for a specific column in a column family

– ("column name" and "qualifier" mean the same thing)

• Optionally you can set a timestamp for a cell

Put add(byte[] family, byte[] qualifier, long ts, byte[] value)

Put add(byte[] family, byte[] qualifier, byte[] value)


Put Operation –Single Put Example

byte [] tableName = Bytes.toBytes("/path/Shopping"); byte [] itemsCF = Bytes.toBytes(“items"); byte [] penCol = Bytes.toBytes (“pens”); byte [] noteCol = Bytes.toBytes (“notes”); byte [] eraserCol = Bytes.toBytes (“erasers”); HTableInterface table = new HTable(hbaseConfig, tableName);

Put put = new Put(“mike”); put.add(itemsCF, penCol, Bytes.toBytes(5l)); put.add(itemsCF, noteCol, Bytes.toBytes(5l)); put.add(itemsCF, eraserCol, Bytes.toBytes(2l));

table.put(put);

Adding multiple column values to a row


Bytes class

– org.apache.hadoop.hbase.util.Bytes– Provides methods to convert Java types to and from byte[] arrays– Support for

• String, boolean, short, int, long, double, and float

– Example:

http://hbase.apache.org/0.94/apidocs/org/apache/hadoop/hbase/util/Bytes.html

byte [] bytesTablePath = Bytes.toBytes("/path/Shopping");

String myTable = Bytes.toString(bytesTablePath);

byte [] amountBytes = Bytes.toBytes(1000l);

long amount = Bytes.toLong(amount);

http://hbase.apache.org/0.94/apidocs/org/apache/hadoop/hbase/util/Bytes.html


Get Operation – Single Get Examplebyte [] tableName = Bytes.toBytes("/path/Shopping");byte [] itemsCF = Bytes.toBytes(“stock");byte [] penCol = Bytes.toBytes (“pens”);HTableInterface table = new HTable(hbaseConfig, tableName);

Get get = new Get(“Mike”);

get.addColumn(itemsCF, penCol);

Result result = myTable.get(get);

byte[] val = result.getValue(itemsCF, penCol);

System.out.println("Value: " + Bytes.toLong(val));


Get Operation – Add And Set methods

• Using just a get object will return everything for a row.• To narrow down results call add

– addFamily: get all columns for a specific family– addColumn: get a specific column

• To further narrow down results, specify more details via one or more set calls then call add– setTimeRange: retrieve columns within a specific range of version timestamps– setTimestamp: retrieve columns with a specific timestamp– setMaxVersions: set the number of versions of each column to be returned– setFilter: add a filter

get.addColumn(columnFamilyName, columnName1);


Result – Retrieve A Value From A Result

public static final byte[] ITEMS_CF= Bytes.toBytes("items");public static final byte[] PENS_COL = Bytes.toBytes(“pens");

Get g = new Get(Bytes.toBytes(“Adam”)); g.addColumn(ITEMS_CF , PENS_COL); Result result = table.get(g); byte[] b = result.getValue(ITEMS_CF, PENS_COL);

long valueInColumn = Bytes.toLong(b);

http://hbase.apache.org/0.94/apidocs/org/apache/hadoop/hbase/client/Result.html

Items:pens Items:notepads Items:erasers

Adam 18 7 10


Other APIs

• Not covering append, delete, and scan• Not covering administrative APIs

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Agenda



Tables and Files in a Unified Storage Layer

MapR Filesystem is an integrated system– Tables and Files in a unified filesystem, based on

MapR’s enterprise-grade storage layer.

HBase

JVM

HDFS

JVM

ext3 FS

Disks

Apache HBase on Hadoop

HBase

JVM

Apache HBase onMapR Filesystem

MapR-FS

Disks

HDFS API

M7 Tables Integratedinto Filesystem

MapR-FS

Disks

HBase API HDFS API


Portability• MapR tables use the HBase data model and API• Apache HBase applications work as-is on MapR tables

– No need to recompile– No vendor lock-in

MapR-FS

Disks

HBase API HDFS API


MapR M7 Table Storage

• Table regions live inside a MapR container– Served by MapR fileserver service running on nodes– HBase RegionServer and HBase Master services are not required

Region

Container

Key colB

colC

val val

val

ClientNodes

Region

Key colB

colC

val val

val

Region

Container

Key colB

colC

val val

val

Region

Key colB

colC

val val

val


MapR Tables vs. HBase

• Compaction delays• Manual administration• Poor reliability• Lengthy disaster recovery

• No compaction delays• Easy administration• Strong consistency• Rapid recovery• 2x Cassandra performance• 3x Hbase performance

Other NoSQL

Service Disruptions 24x7 Uptime


MapR M7 vs. HBase – Mixed Load (50-50)


Agenda



Example: Employee Database

• Column Family: Base– lastName– firstName– address– SSN

• Column Family: salary– ‘dynamic’ columns– year:salary

• Row key– lastName:firstName? Not unique– Unique id? Can’t search easily– lastName:firstName:id? Can’t search by id

34


Source: “employee class”

public class Employee {String key;

String lastName, firstName, address; String ssn; Map<Integer, Integer> salary;…}

35


Source: ‘schema’byte[] BASE_CF = Bytes.toBytes("base");byte[] SALARY_CF = Bytes.toBytes("salary");byte[] FIRST_COL = Bytes.toBytes("firstName");byte[] LAST_COL = Bytes.toBytes("lastName");byte[] ADDRESS_COL = Bytes.toBytes("address");byte[] SSN_COL = Bytes.toBytes("ssn");String tableName = userdirectory + "/" + shortName;byte[] TABLE_NAME = Bytes.toBytes(tableName);

36


Source: “get table”HTablePool pool = new HTablePool();table = pool.getTable(TABLE_NAME);return table;

37


Source: “get row”

• Whole rowGet g = new Get(Bytes.toBytes(key));Result result = getTable().get(g);

• Just base column family Get g = new Get(Bytes.toBytes(key)); g.addFamily(BASE_CF); Result result = getTable().get(g);

38


Source: “parse row”Employee e = new Employee();e.setKey(Bytes.toString(r.getRow()));e.setLastName(getString(r, BASE_CF, LAST_COL));e.setFirstName(getString(r,BASE_CF, FIRST_COL));e.setAddress(getString(r,BASE_CF, ADDRESS_COL));e.setSsn(getString(r,BASE_CF, SSN_COL));

String getString(Result r, byte[] cf, byte[] col) { byte[] b = r.getValue(cf, col); if (b != null) return Bytes.toString(b); else return "";}

39


Source: “parse row”//get salary informationMap<byte[], byte[]> m = r.getFamilyMap(SALARY_CF);Iterator<Map.Entry<byte[], byte[]>> i = m.entrySet().iterator();while (i.hasNext()) { Map.Entry<byte[], byte[]> entry = i.next(); Integer year = Integer.parseInt(Bytes.toString(entry.getKey())); Integer amt = Integer.parseInt(Bytes.toString( entry.getValue())); e.getSalary().put(year, amt);}

40


Example

• Create a table using MCS• Create a table and column families using maprcli

41

$ maprcli table create -path /user/keys/employees$ maprcli table cf create -path /user/keys/employees -cfname base$ maprcli table cf create -path /user/keys/employees -cfname salary


Example

• Populate with sample data using hbase shell

42

hbase> put '/user/keys/employees', 'k1', 'base:lastName', 'William'> put '/user/keys/employees', 'k1', 'base:firstName', 'John'> put '/user/keys/employees', 'k1', 'base:address', '123 street, springfield, VA'> put '/user/keys/empoyees', 'k1', 'base:ssn', '999-99-9999'> put '/user/keys/employees', 'k1', 'salary:2010', '90000’> put '/user/keys/employees', 'k1', 'salary:2011', '91000’> put '/user/keys/employees', 'k1', 'salary:2012', '92000’> put '/user/keys/employees', 'k1', 'salary:2013', '93000’….….


Example

• Fetch record using java program

43

$ ./run employees get k1Use command get against table /user/keys/employeesEmployee record:Employee [key=k1, lastName=William, firstName=John, address=123 first street, springfield, VA, ssn=999-99-9999, salary={2010=90000, 2011=91000, 2012=92000, 2013=93000}]


What Didn’t I Consider?

45


What Didn’t I Consider?

• Row Key• Secondary ways of searching

– Other tables as indexes?

• Long term data evolution– Avro?– Protobufs?

• Security– SSN is sensitive– Salary looks kind of sensitive

46


Agenda



MapR Tables Security

• Access Control Expressions (ACEs)– Boolean logic to control access at table, column family, & column level


ACE Highlights

• Creator of table has all rights by default– Others have none

• Can grant admin rights without granting read/write rights• Defaults for column families set at table level• Access to data depends on column family and column access

controls• Boolean logic

49


MapR Tables Security

• Leverages MapR security when enabled– Wire level authentication– Wire level encryption– Trivial to configure

• Most reasonable settings by default• No Kerberos required!

– Portable• No MapR specific APIs

50


Example

• Enable cluster security

• Yes, that’s it!– Now all Web UI and CLI access requires authentication– Traffic is now authenticated using encrypted credentials– Most traffic is encrypted and bulk data transfer traffic can be encrypted

51

# configure.sh –C hostname –Z hostname -secure –genkeys


Example

• Fetch record using java program when not authenticated

52

$ ./run employees get k1Use command get against table /user/keys/employees14/03/14 18:42:39 ERROR fs.MapRFileSystem: Exception while trying to get currentUserjava.io.IOException: failure to login: Unable to obtain MapR credentials


Example

• Fetch record using java program

53

$ maprlogin password[Password for user 'keys' at cluster 'my.cluster.com': ] MapR credentials of user 'keys' for cluster 'my.cluster.com' are written to '/tmp/maprticket_1000'$ ./run employees get k1Use command get against table /user/keys/employeesEmployee record:Employee [key=k1, lastName=William, firstName=John, address=123 first street, springfield, VA, ssn=999-99-9999, salary={2010=90000, 2011=91000, 2012=92000, 2013=93000}]


Example

• Fetch record using java program as someone not authorized to table

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$ maprlogin password[Password for user 'fred' at cluster 'my.cluster.com': ] MapR credentials of user 'fred' for cluster 'my.cluster.com' are written to '/tmp/maprticket_2001'$ ./run /user/keys/employees get k1Use command get against table /user/keys/employees2014-03-14 18:49:20,2787 ERROR JniCommon fs/client/fileclient/cc/jni_common.cc:7318 Thread: 139674989631232 Error in DBGetRPC for table /user/keys/employees, error: Permission denied(13)Exception in thread "main" java.io.IOException: Error: Permission denied(13)


Example

• Set ACEs to allow read to base information but not salary • Fetch whole record using java program

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$ ./run /user/keys/employees get k1Use command get against table /user/keys/employees2014-03-14 18:53:15,0806 ERROR JniCommon fs/client/fileclient/cc/jni_common.cc:7318 Thread: 139715048077056 Error in DBGetRPC for table /user/keys/employees, error: Permission denied(13)Exception in thread "main" java.io.IOException: Error: Permission denied(13)


Example

• Set ACEs to allow read to base information but not salary • Fetch just base record using java program

56

$ ./run employees getbase k1Use command get against table /user/keys/employeesEmployee record:Employee [key=k1, lastName=William, firstName=John, address=123 first street, springfield, VA, ssn=999-99-9999, salary={}]


Agenda



References

• http://www.mapr.com/blog/getting-started-mapr-security-0 • http://www.mapr.com/• http://hadoop.apache.org/• http://hbase.apache.org/• http://tech.flurry.com/2012/06/12/137492485/ • http://en.wikipedia.org/wiki/Lexicographical_order • Hbase in Action, Nick Dimiduck, Amandeep Khurana• HBase: The Definitive Guide, Lars George• Note: this presentation includes materials from the MapR HBase

training classes

http://www.mapr.com/blog/getting-started-mapr-security-0

http://www.mapr.com/

http://hadoop.apache.org/



http://hbase.apache.org/

http://hbase.apache.org/

http://tech.flurry.com/2012/06/12/137492485/



http://en.wikipedia.org/wiki/Lexicographical_order

http://en.wikipedia.org/wiki/Lexicographical_order


www.hbasebook.com

http://www.hbasebook.com/


https://github.com/larsgeorge/hbase-book


Agenda



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