starbase

HBase Stargate (REST API) client wrapper for Python.

Read the official documentation of the Stargate.

Description

starbase is (at the moment) a client implementation of the Apache HBase REST API (Stargate).

What you have to know

Beware, that REST API is slow (not to blame on this library!). If you can operate with HBase directly better do so.

Prerequisites

You need to have Hadoop, HBase, Thrift and Stargate running. If you want to make it easy for yourself, read my instructions on installing Cloudera manager (free) on Ubuntu 12.04 LTS here or there.

Once you have everything installed and running (by default Stargate runs on 127.0.0.1:8000), you should be able to run src/starbase/client/test.py without problems (UnitTest).

Supported Python versions

• 2.6.8 and up
• 2.7
• 3.3

Features

Project is still in development, thus not all the features of the API are available.

Features implemented

• Connect to Stargate.
• Show software version.
• Show cluster version.
• Show cluster status.
• List tables.
• Retrieve table schema.
• Retrieve table meta data.
• Get a list of tables' column families.
• Create a table.
• Delete a table.
• Alter table schema.
• Insert (PUT) data into a single row (single or multiple columns).
• Update (POST) data of a single row (single or multiple columns).
• Select (GET) a single row from table, optionally with selected columns only.
• Delete (DELETE) a single row by id.
• Batch insert (PUT).
• Batch update (POST).
• Basic HTTP auth is working. You could provide a login and a password to the connection.
• Retrive all rows in a table (table scanning).

Features in-development

• Table scanning.
• Syntax globbing.

Installation

Install latest stable version from PyPI.

$ pip install starbase

Or latest stable version from github.

$ pip install -e git+https://github.com/barseghyanartur/starbase@stable#egg=starbase

Usage and examples

Operating with API starts with making a connection instance.

Required imports

from starbase import Connection

Create a connection instance

Defaults to 127.0.0.1:8000. Specify host and port arguments when creating a connection instance, if your settings are different.

c = Connection()

With customisations, would look simlar to the following.

c = Connection(host='192.168.88.22', port=8001)

Show tables

Assuming that there are two existing tables named table1 and table2, the following would be printed out.

c.tables()

Output.

['table1', 'table2']

Operating with table schema

Whenever you need to operate with a table (also, if you need to create one), you need to have a table instance created.

Create a table instance (note, that at this step no table is created).

t = c.table('table3')

Create a new table

Assuming that no table named table3 yet exists in the database, create a table named table3 with columns (column families) column1, column2, column3 (this is the point where the table is actually created). In the example below, column1, column2 and column3 are column families (in short - columns). Columns are declared in the table schema.

t.create('column1', 'column2', 'column3')

Output.

201

Check if table exists

t.exists()

Output.

True

Show table columns (column families)

t.columns()

Output.

['column1', 'column2', 'column3']

Add columns to the table

Add columns given (column4, column5, column6, column7).

t.add_columns('column4', 'column5', 'column6', 'column7')

Output.

200

Drop columns from table

Drop columns given (column6, column7).

t.drop_columns('column6', 'column7')

Output.

201

Drop entire table schema

t.drop()

Output.

200

Operating with table data

Insert data into a single row

HBase is a key/value store. In HBase columns (also named column families) are part of declared table schema and have to be defined when a table is created. Columns have qualifiers, which are not declared in the table schema. Number of column qualifiers is not limited.

Within a single row, a value is mapped by a column family and a qualifier (in terms of key/value store concept). Value might be anything castable to string (JSON objects, data structures, XML, etc).

In the example below, key11, key12, key21, etc. - are the qualifiers. Obviously, column1, column2 and column3 are column families.

Column families must be composed of printable characters. Qualifiers can be made of any arbitrary bytes.

Table rows are identified by row keys - unique identifiers (UID or so called primary key). In the example below, my-key-1 is the row key (UID).

То recap all what's said above, HBase maps (row key, column family, column qualifier and timestamp) to a value.

t.insert(
    'my-key-1',
    {
        'column1': {'key11': 'value 11', 'key12': 'value 12',
                    'key13': 'value 13'},
        'column2': {'key21': 'value 21', 'key22': 'value 22'},
        'column3': {'key32': 'value 31', 'key32': 'value 32'}
    }
    )

Output.

200

Note, that you may also use the native way of naming the columns and cells (qualifiers). Result of the following would be equal to the result of the previous example.

t.insert(
    'my-key-1',
    {
        'column1:key11': 'value 11', 'column1:key12': 'value 12',
        'column1:key13': 'value 13',
        'column2:key21': 'value 21', 'column2:key22': 'value 22',
        'column3:key32': 'value 31', 'column3:key32': 'value 32'
    }
    )

Output.

200

Update row data

t.update(
    'my-key-1',
    {'column4': {'key41': 'value 41', 'key42': 'value 42'}}
    )

Output.

200

Remove row, row column or row cell data

Remove a row cell (qualifier) data. In the example below, the my-key-1 is table row UID, column4 is the column family and the key41 is the qualifier. Note, that only qualifer data (for the row given) is being removed. All other possible qualifiers of the column column4 will remain untouched.

t.remove('my-key-1', 'column4', 'key41')

Output.

200

Remove a row column (column family) data. Note, that at this point, the entire column data (data of all qualifiers for the row given) is being removed.

t.remove('my-key-1', 'column4')

Output.

200

Remove an entire row data. Note, that in this case, entire row data, along with all columns and qualifiers for the row given, is being removed.

t.remove('my-key-1')

Output.

200

Fetch table data

Fetch a single row data with all columns and qualifiers.

t.fetch('my-key-1')

Output.

{
    'column1': {'key11': 'value 11', 'key12': 'value 12', 'key13': 'value 13'},
    'column2': {'key21': 'value 21', 'key22': 'value 22'},
    'column3': {'key32': 'value 31', 'key32': 'value 32'}
}

Fetch a single row data with selected columns (limit to column1 and column2 columns and all their qualifiers).

t.fetch('my-key-1', ['column1', 'column2'])

Output.

{
    'column1': {'key11': 'value 11', 'key12': 'value 12', 'key13': 'value 13'},
    'column2': {'key21': 'value 21', 'key22': 'value 22'},
}

Narrow the result set even more (limit to qualifiers key1 and key2 of column column1 and qualifier key32 of column column3).

t.fetch('my-key-1', {'column1': ['key11', 'key13'], 'column3': ['key32']})

Output.

{
    'column1': {'key11': 'value 11', 'key13': 'value 13'},
    'column3': {'key32': 'value 32'}
}

Note, that you may also use the native way of naming the columns and cells (qualifiers). Example below does exactly the same as example above.

t.fetch('my-key-1', ['column1:key11', 'column1:key13', 'column3:key32'])

Output.

{
    'column1': {'key11': 'value 11', 'key13': 'value 13'},
    'column3': {'key32': 'value 32'}
}

If you set the perfect_dict argument to False, you'll get the native data structure.

t.fetch(
    'my-key-1',
    ['column1:key11', 'column1:key13', 'column3:key32'],
    perfect_dict=False
    )

Output.

{
    'column1:key11': 'value 11',
    'column1:key13': 'value 13',
    'column3:key32': 'value 32'
}

Batch operations with table data

Batch operations (insert and update) work similar to normal insert and update, but are done in a batch. You are advised to operate in batch as much as possible.

Batch insert

In the example below, we will insert 5000 records in a batch.

data = {
    'column1': {'key11': 'value 11', 'key12': 'value 12', 'key13': 'value 13'},
    'column2': {'key21': 'value 21', 'key22': 'value 22'},
}
b = t.batch()
if b:
    for i in range(0, 5000):
        b.insert('my-key-%s' % i, data)
    b.commit(finalize=True)

Output.

{'method': 'PUT', 'response': [200], 'url': 'table3/bXkta2V5LTA='}

Batch update

In the example below, we will update 5000 records in a batch.

data = {
    'column3': {'key31': 'value 31', 'key32': 'value 32'},
}
b = t.batch()
if b:
    for i in range(0, 5000):
        b.update('my-key-%s' % i, data)
    b.commit(finalize=True)

Output.

{'method': 'POST', 'response': [200], 'url': 'table3/bXkta2V5LTA='}

Note: The table batch method accepts an optional size argument (int). If set, an auto-commit is fired each the time the stack is full.

Table data search (row scanning)

Table scanning is in development (therefore, the scanning API will likely be changed). Result set returned is a generator.

Fetch all rows

t.fetch_all_rows()

Output.

<generator object results at 0x28e9190>

Fetch rows with a filter given

rf = '{"type": "RowFilter", "op": "EQUAL", "comparator": {"type": "RegexStringComparator", "value": "^row_1.+"}}'
t.fetch_all_rows(with_row_id=True, filter_string=rf)

Output.

<generator object results at 0x28e9190>

More information on table operations

By default, prior further execution of the fetch, insert, update, remove (table row operations) methods, it's being checked whether the table exists or not. That's safe, but comes in cost of an extra (light though) HTTP request. If you're absolutely sure you want to avoid those checks, you can disable them. It's possible to disable each type of row operation, by setting the following properties of the table instance to False: check_if_exists_on_row_fetch, check_if_exists_on_row_insert, check_if_exists_on_row_remove and check_if_exists_on_row_update.

t.check_if_exists_on_row_fetch = False
t.fetch('row1')

It's also possible to disable them all at once, by calling the disable_row_operation_if_exists_checks method of the table instance.

t.disable_row_operation_if_exists_checks()
t.remove('row1')

Same goes for table scanner operations. Setting the value of check_if_exists_on_scanner_operations of a table instance to False, skips the checks for scanner operations.

t.check_if_exists_on_scanner_operations = False
t.fetch_all_rows(flat=True)

Exception handling

Methods that accept fail_silently argument are listed per class below.

starbase.client.connection.Connection

• cluster_version
• cluster_status
• drop_table
• tables
• table_exists
• version

starbase.client.table.Table

• add_columns
• batch
• create
• drop
• drop_columns
• exists
• insert
• fetch
• fetch_all_rows
• regions
• remove
• schema
• update

starbase.client.table.Batch

• commit
• insert
• update

starbase.client.transport.HttpRequest

Class starbase.client.table.Batch accepts fail_silently as a constructor argument.

More examples

Show software version

print connection.version

Output.

{u'JVM': u'Sun Microsystems Inc. 1.6.0_43-20.14-b01',
 u'Jersey': u'1.8',
 u'OS': u'Linux 3.5.0-30-generic amd64',
 u'REST': u'0.0.2',
 u'Server': u'jetty/6.1.26'}

Show cluster version

print connection.cluster_version

Output.

u'0.94.7'

Show cluster status

print connection.cluster_status

Output.

{u'DeadNodes': [],
 u'LiveNodes': [{u'Region': [{u'currentCompactedKVs': 0,
 ...
 u'regions': 3,
 u'requests': 0}

Show table schema

print table.schema()

Output.

{u'ColumnSchema': [{u'BLOCKCACHE': u'true',
   u'BLOCKSIZE': u'65536',
 ...
   u'IS_ROOT': u'false',
 u'name': u'messages'}

Print table metadata

print table.regions()

Failed requests

By default, number of retries for a failed request is equal to zero. That means, the request isn't being repeated if failed. It's possible to retry a failed request (for instance, in case of timeouts, etc).

In order to do that, two additional arguments of the starbase.client.connection.Connection have been introduced:

• retries (int)
• retry_delay (int)

c = Connection(
    retries = 3, # Retry 3 times
    retry_delay = 5 # Wait for 5 seconds between retries
    )

Beware! Number of retries can cause performance issues (lower responsiveness) of your application. At the moment, failed requests, such as deletion of non-existing column, row or a table, are handled in the same way and would all cause a retry. This likely will change in future (smarter detection of failures worth to retry a request).

License

GPL 2.0/LGPL 2.1

Support

For any issues contact me at the e-mail given in the Author section.

Author

Artur Barseghyan <artur.barseghyan@gmail.com>