Introduction to HBase - Phoenix HUG 5/14
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Transcript of 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
© 2014 MapR Technologies 2
Agenda
• HBase Overview• HBase APIs• MapR Tables• Example• Securing tables
© 2014 MapR Technologies 3
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
© 2014 MapR Technologies 4
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
© 2014 MapR Technologies 5
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
© 2014 MapR Technologies 6
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
© 2014 MapR Technologies 7
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
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ZooKeeperZooKeeper
Region Server Data Node
Region Server Data Node
Region Server Data Node
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
Region Server Data Node
Region Server Data Node
Region Server Data Node
Region Server Data Node
NameNode
A B
ZooKeeper
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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
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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
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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?
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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
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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
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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
© 2014 MapR Technologies 15
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
© 2014 MapR Technologies 16
Agenda
• HBase Overview• HBase APIs• MapR Tables• Example• Securing tables
© 2014 MapR Technologies 17
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
© 2014 MapR Technologies 18
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);
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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
© 2014 MapR Technologies 20
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)
© 2014 MapR Technologies 21
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
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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);
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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));
© 2014 MapR Technologies 24
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);
© 2014 MapR Technologies 25
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
© 2014 MapR Technologies 26
Other APIs
• Not covering append, delete, and scan• Not covering administrative APIs
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© 2014 MapR Technologies 27
Agenda
• HBase Overview• HBase APIs• MapR Tables• Example• Securing tables
© 2014 MapR Technologies 28
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
© 2014 MapR Technologies 29
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
© 2014 MapR Technologies 30
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
© 2014 MapR Technologies 31
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
© 2014 MapR Technologies 32
MapR M7 vs. HBase – Mixed Load (50-50)
© 2014 MapR Technologies 33
Agenda
• HBase Overview• HBase APIs• MapR Tables• Example• Securing tables
© 2014 MapR Technologies 34
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
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© 2014 MapR Technologies 35
Source: “employee class”
public class Employee {String key;
String lastName, firstName, address; String ssn; Map<Integer, Integer> salary;…}
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© 2014 MapR Technologies 36
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);
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© 2014 MapR Technologies 37
Source: “get table”HTablePool pool = new HTablePool();table = pool.getTable(TABLE_NAME);return table;
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© 2014 MapR Technologies 38
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);
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© 2014 MapR Technologies 39
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 "";}
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© 2014 MapR Technologies 40
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);}
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© 2014 MapR Technologies 41
Example
• Create a table using MCS• Create a table and column families using maprcli
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$ 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
© 2014 MapR Technologies 42
Example
• Populate with sample data using hbase shell
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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’….….
© 2014 MapR Technologies 43
Example
• Fetch record using java program
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$ ./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}]
© 2014 MapR Technologies 45
What Didn’t I Consider?
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© 2014 MapR Technologies 46
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
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© 2014 MapR Technologies 47
Agenda
• HBase Overview• HBase APIs• MapR Tables• Example• Securing tables
© 2014 MapR Technologies 48
MapR Tables Security
• Access Control Expressions (ACEs)– Boolean logic to control access at table, column family, & column level
© 2014 MapR Technologies 49
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
© 2014 MapR Technologies 50
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
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© 2014 MapR Technologies 51
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
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# configure.sh –C hostname –Z hostname -secure –genkeys
© 2014 MapR Technologies 52
Example
• Fetch record using java program when not authenticated
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$ ./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
© 2014 MapR Technologies 53
Example
• Fetch record using java program
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$ 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}]
© 2014 MapR Technologies 54
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)
© 2014 MapR Technologies 55
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)
© 2014 MapR Technologies 56
Example
• Set ACEs to allow read to base information but not salary • Fetch just base record using java program
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$ ./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={}]
© 2014 MapR Technologies 57
Agenda
• HBase Overview• HBase APIs• MapR Tables• Example• Securing tables
© 2014 MapR Technologies 58
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
© 2014 MapR Technologies 60
https://github.com/larsgeorge/hbase-book
© 2014 MapR Technologies 61
Agenda
• HBase Overview• HBase APIs• MapR Tables• Example• Securing tables
© 2014 MapR Technologies 62
Q & A
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HBase Architecture