starbase 0.2 (beta)

Python client for HBase Stargate REST server

Create a new table

Create a table named table3 with columns 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')
201

Check if table exists

>>> t.exists()
True

Show table columns

>>> t.columns()
['column1', 'column2', 'column3']

Add columns to the table

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

>>> t.add_columns('column4', 'column5', 'column6', 'column7')
200

Drop columns from table

Drop columns given (column6, column7).

>>> t.drop_columns('column6', 'column7')
201

Drop entire table schema

>>> t.drop()
200

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, key1, 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'}
>>>     }
>>> )
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-1a',
>>>     {
>>>         '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'
>>>     }
>>> )
200

Update row data

>>> t.update(
>>>     'my-key-1',
>>>     {'column4': {'key41': 'value 41', 'key42': 'value 42'}}
>>> )
200

Remove row, row column or row cell

Remove a row cell (qualifier). In the example below, the my-key-1 is table row UID, column4 is the column family and the key41 is the qualifier.

>>> t.remove('my-key-1', 'column4', 'key41')
200

Remove a row column (column family).

>>> t.remove('my-key-1', 'column4')
200

Remove an entire row.

>>> t.remove('my-key-1')
200

Fetch table data

Fetch a single row with all columns.

>>> t.fetch('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'}
}

Fetch a single row with selected columns (limit to column1 and column2 columns).

>>> t.fetch('my-key-1', ['column1', 'column2'])
{
    '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 cells key1 and key2 of column column1 and cell key32 of column column3).

>>> t.fetch('my-key-1', {'column1': ['key11', 'key13'], 'column3': ['key32']})
{
    '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'])
{
    '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)
{
    'column1:key11': 'value 11', 'column1:key13': 'value 13',
    'column3:key32': 'value 32'
}

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()
>>> for i in range(0, 5000):
>>>     b.insert('my-key-%s' % i, data)
>>> b.commit(finalize=True)
{'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()
>>> for i in range(0, 5000):
>>>     b.update('my-key-%s' % i, data)
>>> b.commit(finalize=True)
{'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.