# hbase-jruby *hbase-jruby* is a simple JRuby binding for HBase. *hbase-jruby* provides the followings: - Easy, Ruby-esque interface for the fundamental HBase operations - ActiveRecord-like method chaining for data retrieval - Automatic Hadoop/HBase dependency resolution ## Installation gem install hbase-jruby ## A quick example ```ruby require 'hbase-jruby' # Load required JAR files from CDH distribution using Maven HBase.resolve_dependency! 'cdh4.3.0' # Connect to HBase on localhost hbase = HBase.new # Define table schema for easier data access hbase.schema = { # Schema for `book` table book: { # Columns in cf1 family cf1: { title: :string, # String (UTF-8) author: :string, category: :string, year: :short, # Short integer (2-byte) pages: :int, # Integer (4-byte) price: :bigdecimal, # BigDecimal weight: :float, # Double-precision floating-point number in_print: :boolean, # Boolean (true | false) image: :raw # Java byte array; no automatic type conversion }, # Columns in cf2 family cf2: { summary: :string, reviews: :fixnum, # Long integer (8-byte) stars: :fixnum, /^comment\d+/ => :string } } } # Create book table with two column families table = hbase[:book] unless table.exists? table.create! cf1: { min_versions: 2 }, cf2: { bloomfilter: :rowcol, versions: 5 } end # PUT table.put 1, title: 'The Golden Bough: A Study of Magic and Religion', author: 'Sir James G. Frazer', category: 'Occult', year: 1890, pages: 1006, price: BigDecimal('21.50'), weight: 3.0, in_print: true, image: File.open('thumbnail.png', 'rb') { |f| f.read }.to_java_bytes, summary: 'A wide-ranging, comparative study of mythology and religion', reviews: 52, stars: 226, comment1: 'A must-have', comment2: 'Rewarding purchase' # GET book = table.get(1) title = book[:title] comment2 = book[:comment2] as_hash = book.to_h # SCAN table.range(0..100) .project(:cf1, :reviews, :summary) .filter(year: 1880...1900, in_print: true, category: ['Comics', 'Fiction', /cult/i], price: { lt: BigDecimal('30.00') }, summary: /myth/i) .each do |book| # Update columns table.put book.rowkey, price: book[:price] + BigDecimal('1') # Atomic increment table.increment book.rowkey, reviews: 1, stars: 5 # Delete two columns table.delete book.rowkey, :comment1, :comment2 end # Delete row table.delete 1 ``` ## Setting up ### Resolving Hadoop/HBase dependency To be able to access HBase from JRuby, Hadoop/HBase dependency must be satisfied. This can be done by either setting up CLASSPATH variable beforehand or by `require`ing relevant JAR files after launching JRuby. ### `HBase.resolve_dependency!` Well, there's an easier way. Call `HBase.resolve_dependency!` helper method passing one of the arguments listed below. | Argument | Dependency | Default version | Required executable | | ---------- | ------------------------ | --------------- | ------------------- | | cdh4.3[.*] | Cloudera CDH4.3 | cdh4.3.0 | mvn | | cdh4.2[.*] | Cloudera CDH4.2 | cdh4.2.1 | mvn | | cdh4.1[.*] | Cloudera CDH4.1 | cdh4.1.4 | mvn | | cdh3[u*] | Cloudera CDH3 | cdh3u6 | mvn | | 0.95[.*] | Apache HBase 0.95 | 0.95.0 | mvn | | 0.94[.*] | Apache HBase 0.94 | 0.94.7 | mvn | | 0.92[.*] | Apache HBase 0.92 | 0.92.2 | mvn | | *POM PATH* | Custom Maven POM file | - | mvn | | `:local` | Local HBase installation | - | hbase | (Default version is used when an argument prefix is given without specific patch version. e.g. `cdh4.2` defaults to `cdh4.2.0`) #### Examples ```ruby # Load JAR files from CDH4 using Maven HBase.resolve_dependency! 'cdh4.3.0' HBase.resolve_dependency! 'cdh4.2' # Load JAR files of HBase 0.94.x using Maven HBase.resolve_dependency! '0.94.7' HBase.resolve_dependency! '0.94.2', verbose: true # Dependency resolution with custom POM file HBase.resolve_dependency! '/path/to/my/pom.xml' HBase.resolve_dependency! '/path/to/my/pom.xml', profile: 'trunk' # Load JAR files from local HBase installation # (equivalent to: export CLASSPATH=$CLASSPATH:`hbase classpath`) HBase.resolve_dependency! :local ``` (If you're behind an http proxy, set up your ~/.m2/settings.xml file as described in [this page](http://maven.apache.org/guides/mini/guide-proxies.html)) ### Log4j logs from HBase You may want to suppress (or customize) log messages from HBase. ```ruby # With an external log4j.properties or log4j.xml file HBase.log4j = '/your/log4j.properties' HBase.log4j = '/your/log4j.xml' # With a Hash HBase.log4j = { 'log4j.threshold' => 'ERROR' } ``` ### Connecting to HBase ```ruby # HBase on localhost hbase = HBase.new # HBase on remote host hbase = HBase.new 'hbase.zookeeper.quorum' => 'remote-server.mydomain.net' # Extra configuration hbase = HBase.new 'hbase.zookeeper.quorum' => 'remote-server.mydomain.net', 'hbase.client.retries.number' => 3, 'hbase.client.scanner.caching' => 1000, 'hbase.rpc.timeout' => 120000 # Close HBase connection hbase.close ``` ## Accessing data with HBase::Table instance `HBase#[]` method (or `HBase#table`) returns an `HBase::Table` instance which represents the table of the given name. ```ruby table = hbase.table(:test_table) # Or simply, table = hbase[:test_table] ``` ### Creating a table ```ruby # Drop table if exists table.drop! if table.exists? # Create table with two column families table.create! cf1: {}, cf2: { compression: :snappy, bloomfilter: :row } ``` ## Basic operations ### Defining table schema for easier data access HBase stores everything as plain Java byte arrays. So it's completely up to users to encode and decode column values of various types into and from byte arrays, and that is a quite tedious and error-prone task. To remedy this situation, `hbase-jruby` implements the concept of table schema. Using table schema greatly simplifies the way you access data: - With schema, byte array conversion becomes automatic - It allows you to omit column family names (e.g. `:title` instead of `"cf1:title"`) We'll use the following schema throughout the examples. ```ruby hbase.schema = { # Schema for `book` table book: { # Columns in cf1 family cf1: { title: :string, # String (UTF-8) author: :string, category: :string, year: :short, # Short integer (2-byte) pages: :int, # Integer (4-byte) price: :bigdecimal, # BigDecimal weight: :float, # Double-precision floating-point number in_print: :boolean, # Boolean (true | false) image: :raw # Java byte array; no automatic type conversion }, # Columns in cf2 family cf2: { summary: :string, reviews: :fixnum, # Long integer (8-byte) stars: :fixnum, /^comment\d+/ => :string } } } ``` Columns that are not defined in the schema can be referenced using `FAMILY:QUALIFIER` notation or 2-element Array of column family name (as Symbol) and qualifier, however since there's no type information, they are returned as Java byte arrays, which have to be decoded manually. ### PUT ```ruby # Putting a single row # - Row keys can be of any type, in this case, we use String type table.put 'rowkey1', title: "Hello World", year: 2013 # Putting multiple rows table.put 'rowkey1' => { title: 'foo', year: 2013 }, 'rowkey2' => { title: "bar", year: 2014 }, 'rowkey3' => { title: 'foobar', year: 2015 } # Putting values with timestamps table.put 'rowkey1', title: { 1353143856665 => "Hello world", 1352978648642 => "Goodbye world" }, year: 2013 ``` ### GET ```ruby book = table.get('rowkey1') # Rowkey rowkey = row.rowkey # Rowkey as raw Java byte array rowkey = row.rowkey :string # Rowkey as String # Access columns in schema title = book[:title] author = book[:author] year = book[:year] # Convert to simple Hash hash = book.to_h # Convert to Hash containing all versions of values indexed by their timestamps all_hash = book.to_H # Columns not defined in the schema are returned as Java byte arrays # They need to be decoded manually extra = HBase::Util.from_bytes(:bigdecimal, book['cf2:extra']) # or, simply extra = book.bigdecimal 'cf2:extra' ``` ### Batch-GET ```ruby # Pass an array of row keys as the parameter books = table.get(['rowkey1', 'rowkey2', 'rowkey3']) ``` #### `to_h` `to_h` and `to_H` return the Hash representation of the row. (The latter returns all values with their timestamp) If a column is defined in the schema, it is referenced using its quailifier in Symbol type. If a column is not defined, it is represented as a 2-element Array of column family in Symbol and column qualifier as ByteArray. Even so, to make it easier to reference those columns, an extended version of Hash is returned with which you can also reference them with `FAMILY:QUALIFIER` notation or `[cf, cq]` array notation. ```ruby table.put 1000, title: 'Hello world', # Known column comment100: 'foo', # Known column 'cf2:extra' => 'bar', # Unknown column [:cf2, 10] => 'foobar' # Unknown column, non-string qualifier book = table.get 10000 hash = book.to_h # { # :title => "Hello world", # [:cf2, HBase::ByteArray<0, 0, 0, 0, 0, 0, 0, 10>] => # byte[102, 111, 111, 98, 97, 114]@6f28bb44, # :comment100 => "foo", # [:cf2, HBase::ByteArray<101, 120, 116, 114, 97>] => # byte[98, 97, 114]@77190cfc} # } hash['cf2:extra'] # byte[98, 97, 114]@77190cfc hash[%w[cf2 extra]] # byte[98, 97, 114]@77190cfc hash[[:cf2, HBase::ByteArray['extra']]] # byte[98, 97, 114]@77190cfc hash['cf2:extra'].to_s # 'bar' # Columns with non-string qualifiers must be referenced using 2-element Array notation hash['cf2:10'] # nil hash[[:cf2, 10]] # byte[102, 111, 111, 98, 97, 114]@6f28bb44 hash_with_versions = book.to_H # { # :title => {1369019227766 => "Hello world"}, # [:cf2, HBase::ByteArray<0, 0, 0, 0, 0, 0, 0, 10>] => # {1369019227766 => byte[102, 111, 111, 98, 97, 114]@6f28bb44}, # :comment100 => {1369019227766 => "foo"}, # [:cf2, HBase::ByteArray<101, 120, 116, 114, 97>] => # {1369019227766 => byte[98, 97, 114]@77190cfc}} # } ``` #### Intra-row scan Intra-row scan can be done using `each` method which yields `HBase::Cell` instances. ```ruby # Intra-row scan (all versions) row.each do |cell| family = cell.family qualifier = cell.qualifier :string # Column qualifier as String timestamp = cell.timestamp value = cell.value end # Array of HBase::Cells cells = row.to_a ``` ### DELETE ```ruby # Delete a row table.delete('rowkey1') # Delete all columns in the specified column family table.delete('rowkey1', :cf1) # Delete a column table.delete('rowkey1', :author) # Delete multiple columns table.delete('rowkey1', :author, :title, :image) # Delete a column with empty qualifier. # (!= deleing the entire columns in the family. See the trailing colon.) table.delete('rowkey1', 'cf1:') # Delete a version of a column table.delete('rowkey1', :author, 1352978648642) # Delete multiple versions of a column table.delete('rowkey1', :author, 1352978648642, 1352978649642) # Delete multiple versions of multiple columns # - Two versions of :author # - One version of :title # - All versions of :image table.delete('rowkey1', :author, 1352978648642, 1352978649642, :title, 1352978649642, :image) # Batch delete; combination of aforementioned arguments each given as an Array table.delete(['rowkey1'], ['rowkey2'], ['rowkey3', :author, 1352978648642, 135297864964]) ``` However, the last syntax seems a bit unwieldy when you just wish to delete a few rows. In that case, use simpler `delete_row` method. ```ruby table.delete_row 'rowkey1' table.delete_row 'rowkey1', 'rowkey2', 'rowkey3' ``` ### Checked PUT and DELETE ```ruby table.check(:rowkey, in_print: false) .put(in_print: true, price: BigDecimal('10.0')) table.check(:rowkey, in_print: false) .delete(:price, :image) # Takes the same parameters as those of HBase::Table#delete # except for the first rowkey # https://github.com/junegunn/hbase-jruby#delete ``` ### Atomic increment of column values ```ruby # Atomically increase cf2:reviews by one table.increment('rowkey1', reviews: 1) # Atomically increase two columns by one and five respectively table.increment('rowkey1', reviews: 1, stars: 5) # Increase column values of multiple rows. # - Atomicity is only guaranteed within each row. table.increment 'rowkey1' => { reviews: 1, stars: 5 }, 'rowkey2' => { reviews: 1, stars: 3 } ``` ### SCAN `HBase::Table` itself is an enumerable object. ```ruby # Full scan table.each do |row| p row.to_h end # Returns Enumerator when block is not given table.each.with_index.each_slice(10).to_a ``` ## Scoped access You can control how you retrieve data by chaining the following methods of `HBase::Table` (or `HBase::Scoped`). | Method | Description | |------------------|-----------------------------------------------------------------| | `range` | Specifies the rowkey range of scan | | `project` | To retrieve only a subset of columns | | `filter` | Filtering conditions of scan | | `while` | Allows early termination of scan (server-side) | | `at` | Only retrieve data with the specified timestamp | | `time_range` | Only retrieve data within the specified time range | | `limit` | Limits the number of rows | | `versions` | Limits the number of versions of each column | | `caching` | Sets the number of rows for caching during scan | | `batch` | Limits the maximum number of values returned for each iteration | | `with_java_scan` | *(ADVANCED)* Access Java Scan object in the given block | | `with_java_get` | *(ADVANCED)* Access Java Get object in the given block | Each invocation to these methods returns an `HBase::Scoped` instance with which you can retrieve data with the following methods. | Method | Description | |-------------|-------------------------------------------------------------------------| | `get` | Fetches rows by the given rowkeys | | `each` | Scans the scope of the table (`HBase::Scoped` instance is `Enumerable`) | | `count` | Efficiently counts the number of rows in the scope | | `aggregate` | Performs aggregation using Coprocessor (To be described shortly) | ### Example of scoped access ```ruby import org.apache.hadoop.hbase.filter.RandomRowFilter table.range('A'..'Z'). # Row key range, project(:author). # Select cf1:author column project('cf2'). # Select cf2 family as well filter(category: 'Comics'). # Filter by cf1:category value filter(year: [1990, 2000, 2010]). # Set-inclusion condition on cf1:year filter(weight: 2.0..4.0). # Range filter on cf1:weight filter(RandomRowFilter.new(0.5)). # Any Java HBase filter while(reviews: { gt: 20 }). # Early termination of scan time_range(Time.now - 600, Time.now). # Scan data of the last 10 minutes limit(10). # Limits the size of the result set versions(2). # Only fetches 2 versions for each value batch(100). # Batch size for scan set to 100 caching(1000). # Caching 1000 rows with_java_scan { |scan| # Directly access Java Scan object scan.setCacheBlocks false }. to_a # To Array of HBase::Rows ``` ### *range* `HBase::Scoped#range` method is used to filter rows based on their row keys. ```ruby # 100 ~ 900 (inclusive end) table.range(100..900) # 100 ~ 900 (exclusive end) table.range(100...900) # 100 ~ 900 (exclusive end) table.range(100, 900) # 100 ~ table.range(100) # ~ 900 (exclusive end) table.range(nil, 900) ``` Optionally, prefix filter can be applied as follows. ```ruby # Prefix filter # Row keys with "APPLE" prefix # Start key is automatically set to "APPLE", # stop key "APPLF" to avoid unnecessary disk access table.range(prefix: 'APPLE') # Row keys with "ACE", "BLUE" or "APPLE" prefix # Start key is automatically set to "ACE", # stop key "BLUF" table.range(prefix: ['ACE', 'BLUE', 'APPLE']) # Prefix filter with start key and stop key. table.range('ACE', 'BLUEMARINE', prefix: ['ACE', 'BLUE', 'APPLE']) ``` Subsequent calls to `#range` override the range previously defined. ```ruby # Previous ranges are discarded scope.range(1, 100). range(50..100). range(prefix: 'A'). range(1, 1000) # Same as `scope.range(1, 1000)` ``` ### *filter* You can configure server-side filtering of rows and columns with `HBase::Scoped#filter` calls. Multiple calls have conjunctive effects. ```ruby # Range scanning the table with filters table.range(nil, 1000). filter( # Equality match year: 2013, # Range of numbers or characters: Checks if the value falls within the range weight: 2.0..4.0, author: 'A'..'C' # Will match rows *without* price column price: nil, # Regular expression: Checks if the value matches the regular expression summary: /classic$/i, # Hash: Tests the value with 6 types of operators (:gt, :lt, :gte, :lte, :eq, :ne) reviews: { gt: 100, lte: 200 }, # Array of the aforementioned types: OR condition (disjunctive) category: ['Fiction', 'Comic', /science/i, { ne: 'Political Science' }]). # Multiple calls for conjunctive filtering filter(summary: /instant/i). # Any number of Java filters can be applied filter(org.apache.hadoop.hbase.filter.RandomRowFilter.new(0.5)). each do |record| # ... end ``` ### *while* `HBase::Scoped#while` method takes the same parameters as `filter` method, the difference is that each filtering condition passed to `while` method is wrapped by `WhileMatchFilter`, which aborts scan immediately when the condition is not met at a certain row. See the following example. ```ruby (0...30).each do |idx| table.put idx, year: 2000 + idx % 10 end table.filter(year: { lte: 2001 }).map { |r| r.rowkey :fixnum } # [0, 1, 10, 11, 20, 21] table.while(year: { lte: 2001 }).map { |r| r.rowkey :fixnum } # [0, 1] # Scan terminates immediately when condition not met. ``` ### *project* `HBase::Scoped#project` allows you to fetch only a subset of columns from each row. Multiple calls have additive effects. ```ruby # Fetches cf1:title, cf1:author, and all columns in column family cf2 and cf3 scoped.project(:title, :author, :cf2). project(:cf3) ``` HBase filters can not only filter rows but also columns. Since column filtering can be thought of as a kind of projection, it makes sense to internally apply column filters in `HBase::Scoped#project`, instead of in `HBase::Scoped#filter`, although it's still perfectly valid to pass column filter to filter method. ```ruby # Column prefix filter: # Fetch columns whose qualifiers start with the specified prefixes scoped.project(prefix: 'alice'). project(prefix: %w[alice bob]) # Column range filter: # Fetch columns whose qualifiers within the ranges scoped.project(range: 'a'...'c'). project(range: ['i'...'k', 'x'...'z']) # Column pagination filter: # Fetch columns within the specified intra-scan offset and limit scoped.project(offset: 1000, limit: 10) ``` When using column filters on *fat* rows with many columns, it's advised that you limit the batch size with `HBase::Scoped#batch` call to avoid fetching all columns at once. However setting batch size allows multiple rows with the same row key are returned during scan. ```ruby # Let's say that we have rows with more than 10 columns whose qualifiers start with `str` puts scoped.range(1..100). project(prefix: 'str'). batch(10). map { |row| [row.rowkey(:fixnum), row.count].map(&:to_s).join ': ' } # 1: 10 # 1: 10 # 1: 5 # 2: 10 # 2: 2 # 3: 10 # ... ``` ### Scoped SCAN / GET ```ruby scoped = table.versions(1) # Limits the number of versions .filter(year: 1990...2000) .range('rowkey0'..'rowkey2') # Range of rowkeys. .project('cf1', 'cf2:x') # Projection # Scoped GET # Nonexistent or filtered rows are returned as nils scoped.get(['rowkey1', 'rowkey2', 'rowkey4']) # Scoped SCAN scoped.each do |row| row.each do |cell| # Intra-row scan end end # Scoped COUNT # When counting the number of rows, use `HTable::Scoped#count` # instead of just iterating through the scope, as it internally # minimizes amount of data fetched with KeyOnlyFilter scoped.count # This should be even faster as it dramatically reduces the number of RPC calls scoped.caching(5000).count ``` ## Basic aggregation using coprocessor You can perform some basic aggregation using the built-in coprocessor called `org.apache.hadoop.hbase.coprocessor.AggregateImplementation`. To enable this feature, call `enable_aggregation!` method, which adds the coprocessor to the table. ```ruby table.enable_aggregation! # Just a shorthand notation for # table.add_coprocessor! 'org.apache.hadoop.hbase.coprocessor.AggregateImplementation' ``` Then you can get the sum, average, minimum, maximum, row count, and standard deviation of the projected columns. ```ruby # cf1:a must hold 8-byte integer values table.project(:reviews).aggregate(:sum) table.project(:reviews).aggregate(:avg) table.project(:reviews).aggregate(:min) table.project(:reviews).aggregate(:max) table.project(:reviews).aggregate(:std) table.project(:reviews).aggregate(:row_count) # Aggregation of multiple columns table.project(:reviews, :stars).aggregate(:sum) ``` By default, aggregate method assumes that the projected values are 8-byte integers. For other data types, you can pass your own ColumnInterpreter. ```ruby table.project(:price).aggregate(:sum, MyColumnInterpreter.new) ``` ## Table inspection ```ruby # Table properties table.properties # {:max_filesize => 2147483648, # :readonly => false, # :memstore_flushsize => 134217728, # :deferred_log_flush => false} # Properties of the column families table.families # {"cf"=> # {:blockcache => true, # :blocksize => 65536, # :bloomfilter => "NONE", # :cache_blooms_on_write => false, # :cache_data_on_write => false, # :cache_index_on_write => false, # :compression => "NONE", # :compression_compact => "NONE", # :data_block_encoding => "NONE", # :evict_blocks_on_close => false, # :in_memory => false, # :keep_deleted_cells => false, # :min_versions => 0, # :replication_scope => 0, # :ttl => 2147483647, # :versions => 3}} ``` There are also `raw_` variants of `properties` and `families`. They return properties in their internal String format (mainly used in HBase shell). (See [HTableDescriptor.values](http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HTableDescriptor.html#values) and [HColumnDescriptor.values](http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html#values)) ```ruby table.raw_properties # {"IS_ROOT" => "false", # "IS_META" => "false", # "MAX_FILESIZE" => "2147483648"} table.raw_families # {"cf" => # {"DATA_BLOCK_ENCODING" => "NONE", # "BLOOMFILTER" => "NONE", # "REPLICATION_SCOPE" => "0", # "VERSIONS" => "3", # "COMPRESSION" => "NONE", # "MIN_VERSIONS" => "0", # "TTL" => "2147483647", # "KEEP_DELETED_CELLS" => "false", # "BLOCKSIZE" => "65536", # "IN_MEMORY" => "false", # "ENCODE_ON_DISK" => "true", # "BLOCKCACHE" => "true"}} ``` These String key-value pairs are not really a part of the public API of HBase, and thus might change over time. However, they are most useful when you need to create a table with the same properties as the existing one. ```ruby hbase[:dupe_table].create!(table.raw_families, table.raw_properties) ``` With `regions` method, you can even presplit the new table just like the old one. ```ruby hbase[:dupe_table].create!( table.raw_families, table.raw_properties.merge(splits: table.regions.map { |r| r[:start_key] }.compact)) ``` ## Table administration `HBase#Table` provides a number of *bang_methods!* for table administration tasks. They run synchronously, except when mentioned otherwise (e.g. `HTable#split!`). Some of them take an optional block to allow progress monitoring and come with non-bang, asynchronous counterparts. ### Creation and alteration ```ruby # Create a table with configurable table-level properties table.create!( # 1st Hash: Column family specification { cf1: { compression: snappy }, cf2: { bloomfilter: row } }, # 2nd Hash: Table properties max_filesize: 256 * 1024 ** 2, deferred_log_flush: false, splits: [1000, 2000, 3000] ) # Alter table properties (synchronous with optional block) table.alter!( max_filesize: 512 * 1024 ** 2, memstore_flushsize: 64 * 1024 ** 2, readonly: false, deferred_log_flush: true ) { |progress, total| # Progress report with an optional block puts [progress, total].join('/') } # Alter table properties (asynchronous) table.alter( max_filesize: 512 * 1024 ** 2, memstore_flushsize: 64 * 1024 ** 2, readonly: false, deferred_log_flush: true ) ``` #### List of column family properties http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HColumnDescriptor.html Some of the properties are only available on recent versions of HBase. | Property | Type | Description | |--------------------------|---------------|--------------------------------------------------------------------------------------------------------------------| | `:blockcache` | Boolean | If MapFile blocks should be cached | | `:blocksize` | Fixnum | Blocksize to use when writing out storefiles/hfiles on this column family | | `:bloomfilter` | Symbol/String | Bloom filter type: `:none`, `:row`, `:rowcol`, or uppercase Strings | | `:cache_blooms_on_write` | Boolean | If we should cache bloomfilter blocks on write | | `:cache_data_on_write` | Boolean | If we should cache data blocks on write | | `:cache_index_on_write` | Boolean | If we should cache index blocks on write | | `:compression` | Symbol/String | Compression type: `:none`, `:gz`, `:lzo`, `:lz4`, `:snappy`, or uppercase Strings | | `:compression_compact` | Symbol/String | Compression type: `:none`, `:gz`, `:lzo`, `:lz4`, `:snappy`, or uppercase Strings | | `:data_block_encoding` | Symbol/String | Data block encoding algorithm used in block cache: `:none`, `:diff`, `:fast_diff`, `:prefix`, or uppercase Strings | | `:encode_on_disk` | Boolean | If we want to encode data block in cache and on disk | | `:evict_blocks_on_close` | Boolean | If we should evict cached blocks from the blockcache on close | | `:in_memory` | Boolean | If we are to keep all values in the HRegionServer cache | | `:keep_deleted_cells` | Boolean | If deleted rows should not be collected immediately | | `:min_versions` | Fixnum | The minimum number of versions to keep (used when timeToLive is set) | | `:replication_scope` | Fixnum | Replication scope | | `:ttl` | Fixnum | Time-to-live of cell contents, in seconds | | `:versions` | Fixnum | The maximum number of versions. (By default, all available versions are retrieved.) | #### List of table properties http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HTableDescriptor.html | Property | Type | Description | |-----------------------|---------|---------------------------------------------------------------------------------------------------------| | `:max_filesize` | Fixnum | The maximum size upto which a region can grow to after which a region split is triggered | | `:readonly` | Boolean | If the table is read-only | | `:memstore_flushsize` | Fixnum | The maximum size of the memstore after which the contents of the memstore are flushed to the filesystem | | `:deferred_log_flush` | Boolean | Defer the log edits syncing to the file system | | `:splits` | Array | Region split points | ### Managing column families ```ruby # Add column family table.add_family! :cf3, compression: :snappy, bloomfilter: :row # Alter column family table.alter_family! :cf2, bloomfilter: :rowcol # Remove column family table.delete_family! :cf1 ``` ### Coprocessors ```ruby # Add Coprocessor unless table.has_coprocessor?(cp_class_name1) table.add_coprocessor! cp_class_name1 end table.add_coprocessor! cp_class_name2, path: path, priority: priority, params: params # Remove coprocessor table.remove_coprocessor! cp_class_name1 ``` ### Region splits (asynchronous) ```ruby table.split!(1000) table.split!(2000, 3000) ``` ### Snapshots ```ruby # Returns a list of all snapshot information hbase.snapshots # Table snapshots table.snapshots # Equivalent to # hbase.snapshots.select { |info| info[:table] == table.name } # Creating a snapshot table.snapshot! 'my_table_snapshot' ``` ### Advanced table administration You can perform other types of administrative tasks with the native Java [HBaseAdmin object](http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/HBaseAdmin.html), which can be obtained by `HBase#admin` method. Optionally, a block can be given so that the object is automatically closed at the end of the given block. ```ruby admin = hbase.admin # ... admin.close # Access native HBaseAdmin object within the block hbase.admin do |admin| admin.snapshot 'my_snapshot', 'my_table' admin.cloneSnapshot 'my_snapshot', 'my_clone_table' admin.deleteSnapshot 'my_snapshot' # ... end ``` ## Advanced topics ### Lexicographic scan order HBase stores rows in the lexicographic order of the rowkeys in their byte array representations. Thus the type of row key affects the scan order. ```ruby (1..15).times do |i| table.put i, data table.put i.to_s, data end table.range(1..3).map { |r| r.rowkey :fixnum } # [1, 2, 3] table.range('1'..'3').map { |r| r.rowkey :string } # %w[1 10 11 12 13 14 15 2 3] ``` ### Non-string column qualifier If a column qualifier is not a String, a 2-element Array should be used. ```ruby table.put 'rowkey', [:cf1, 100 ] => "Byte representation of an 8-byte integer", [:cf1, bytes] => "Qualifier is an arbitrary byte array" table.get('rowkey')[:cf1, 100] # ... ``` ### Shorter integers A Ruby Fixnum is an 8-byte integer, which is equivalent `long` type in Java. When you want to use shorter integer types such as int, short, or byte, you can then use the special Hash representation of integers. ```ruby # 4-byte int value as the rowkey table.put({ int: 12345 }, 'cf1:a' => { byte: 100 }, # 1-byte integer 'cf1:b' => { short: 200 }, # 2-byte integer 'cf1:c' => { int: 300 }, # 4-byte integer 'cf1:d' => 400) # Ordinary 8-byte integer row = table.get(int: 12345) ``` The use of these Hash-notations can be minimized if we define table schema as follows. ```ruby hbase.schema[table.name] = { cf1: { a: :byte, b: :short, c: :int, d: :fixnum } } table.put({ int: 12345 }, a: 100, b: 200, c: 300, d: 400) row = table.get(int: 12345) ``` ### Working with byte arrays In HBase, virtually everything is stored as a byte array. Although *hbase-jruby* tries hard to hide the fact, at some point you may need to get your hands dirty with native Java byte arrays. For example, it's [a common practice] [1] to use a composite row key, which is a concatenation of several components of different types. [1]: http://blog.sematext.com/2012/08/09/consider-using-fuzzyrowfilter-when-in-need-for-secondary-indexes-in-hbase/ `HBase::ByteArray` is a boxed class for native Java byte arrays, which makes byte array manipulation much easier. A ByteArray can be created as a concatenation of any number of objects. ```ruby ba = HBase::ByteArray[100, 3.14, {int: 300}, "Hello World"] ``` Then you can slice it and decode each part, ```ruby # Slicing first = ba[0, 8] second = ba[8...16] first.decode(:fixnum) # 100 second.decode(:float) # 3.14 ``` append, prepend more elements to it, ```ruby ba.unshift 200, true ba << { short: 300 } ``` concatenate another ByteArray, ```ruby ba += HBase::ByteArray[1024] ``` or shift decoded objects from it. ```ruby ba.shift(:fixnum) ba.shift(:boolean) ba.shift(:fixnum) ba.shift(:float) ba.shift(:int) ba.shift(:string, 11) # Byte length must be given as Strings are not fixed in size ``` `ByteArray#java` method returns the underlying native Java byte array. ```ruby ba.java # Returns the native Java byte array (byte[]) ``` ## Test ```bash #!/bin/bash # Test HBase 0.94 on localhost export HBASE_JRUBY_TEST_ZK='127.0.0.1' export HBASE_JRUBY_TEST_DIST='0.94' # Test both for 1.8 and 1.9 for v in --1.8 --1.9; do export JRUBY_OPTS=$v rake test done ``` ## Contributing 1. Fork it 2. Create your feature branch (`git checkout -b my-new-feature`) 3. Commit your changes (`git commit -am 'Add some feature'`) 4. Push to the branch (`git push origin my-new-feature`) 5. Create new Pull Request