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User guide

This user guide explores the HappyBase API and should provide you with enough information to get you started. Note that this user guide is intended as an introduction to HappyBase, not to HBase in general. Readers should already have a basic understanding of HBase and its data model.

While the user guide does cover most features, it is not a complete reference guide. More information about the HappyBase API is available from the :doc:`API documentation <api>`.

Establishing a connection

We'll get started by connecting to HBase. Just create a new :py:class:`Connection` instance:

import happybase

connection = happybase.Connection('somehost')

In some setups, the :py:class:`Connection` class needs some additional information about the HBase version it will be connecting to, and which Thrift transport to use. If you're still using HBase 0.90.x, you need to set the compat argument to make sure HappyBase speaks the correct wire protocol. Additionally, if you're using HBase 0.94 with a non-standard Thrift transport mode, make sure to supply the right transport argument. See the API documentation for the :py:class:`Connection` class for more information about these arguments and their supported values.

When a :py:class:`Connection` is created, it automatically opens a socket connection to the HBase Thrift server. This behaviour can be disabled by setting the autoconnect argument to False, and opening the connection manually using :py:meth:`Connection.open`:

connection = happybase.Connection('somehost', autoconnect=False)

# before first use:
connection.open()

The :py:class:`Connection` class provides the main entry point to interact with HBase. For instance, to list the available tables, use :py:meth:`Connection.tables`:

print connection.tables()

Most other methods on the :py:class:`Connection` class are intended for system management tasks like creating, dropping, enabling and disabling tables. See the :doc:`API documentation <api>` for the :py:class:`Connection` class contains more information. This user guide does not cover those since it's more likely you are already using the HBase shell for these system management tasks.

Note

HappyBase also features a connection pool, which is covered later in this guide.

Working with tables

The :py:class:`Table` class provides the main API to retrieve and manipulate data in HBase. In the example above, we already asked for the available tables using the :py:meth:`Connection.tables` method. If there weren't any tables yet, you can create a new one using :py:meth:`Connection.create_table`:

connection.create_table(
    'mytable',
    {'cf1': dict(max_versions=10),
     'cf2': dict(max_versions=1, block_cache_enabled=False),
     'cf3': dict(),  # use defaults
    }
)

Note

The HBase shell is often a better alternative for many HBase administration tasks, since the shell is more powerful compared to the limited Thrift API that HappyBase uses.

The next step is to obtain a :py:class:`.Table` instance to work with. Simply call :py:meth:`Connection.table`, passing it the table name:

table = connection.table('mytable')

Obtaining a :py:class:`Table` instance does not result in a round-trip to the Thrift server, which means application code may ask the :py:class:`Connection` instance for a new :py:class:`Table` whenever it needs one, without negative performance consequences. A side effect is that no check is done to ensure that the table exists, since that would involve a round-trip. Expect errors if you try to interact with non-existing tables later in your code. For this guide, we assume the table exists.

Note

The ‘heavy’ HTable HBase class from the Java HBase API, which performs the real communication with the region servers, is at the other side of the Thrift connection. There is no direct mapping between :py:class:`Table` instances on the Python side and HTable instances on the server side.

Using table ‘namespaces’

If a single HBase instance is shared by multiple applications, table names used by different applications may collide. A simple solution to this problem is to add a ‘namespace’ prefix to the names of all tables ‘owned’ by a specific application, e.g. for a project myproject all tables have names like myproject_XYZ.

Instead of adding this application-specific prefix each time a table name is passed to HappyBase, the table_prefix argument to :py:class:`Connection` can take care of this. HappyBase will prepend that prefix (and an underscore) to each table name handled by that :py:class:`Connection` instance. For example:

connection = happybase.Connection('somehost', table_prefix='myproject')

At this point, :py:meth:`Connection.tables` no longer includes tables in other ‘namespaces’. HappyBase will only return tables with a myproject_ prefix, and will also remove the prefix transparently when returning results, e.g.:

print connection.tables()  # Table "myproject_XYZ" in HBase will be
                           # returned as simply "XYZ"

This also applies to other methods that take table names, such as :py:meth:`Connection.table`:

table = connection.table('XYZ')  # Operates on myproject_XYZ in HBase

The end result is that the table prefix is specified only once in your code, namely in the call to the :py:class:`Connection` constructor, and that only a single change is necessary in case it needs changing.

Retrieving data

The HBase data model is a multidimensional sparse map. A table in HBase contains column families with column qualifiers containing a value and a timestamp. In most of the HappyBase API, column family and qualifier names are specified as a single string, e.g. cf1:col1, and not as two separate arguments. While column families and qualifiers are different concepts in the HBase data model, they are almost always used together when interacting with data, so treating them as a single string makes the API a lot simpler.

Retrieving rows

The :py:class:`Table` class offers various methods to retrieve data from a table in HBase. The most basic one is :py:meth:`Table.row`, which retrieves a single row from the table, and returns it as a dictionary mapping columns to values:

row = table.row('row-key')
print row['cf1:col1']   # prints the value of cf1:col1

The :py:meth:`Table.rows` method works just like :py:meth:`Table.row`, but takes multiple row keys and returns those as (key, data) tuples:

rows = table.rows(['row-key-1', 'row-key-2'])
for key, data in rows:
    print key, data

If you want the results that :py:meth:`Table.rows` returns as a dictionary or ordered dictionary, you will have to do this yourself. This is really easy though, since the return value can be passed directly to the dictionary constructor. For a normal dictionary, order is lost:

rows_as_dict = dict(table.rows(['row-key-1', 'row-key-2']))

…whereas for a :py:class:`OrderedDict`, order is preserved:

from collections import OrderedDict
rows_as_ordered_dict = OrderedDict(table.rows(['row-key-1', 'row-key-2']))

Making more fine-grained selections

HBase's data model allows for more fine-grained selections of the data to retrieve. If you know beforehand which columns are needed, performance can be improved by specifying those columns explicitly to :py:meth:`Table.row` and :py:meth:`Table.rows`. The columns argument takes a list (or tuple) of column names:

row = table.row('row-key', columns=['cf1:col1', 'cf1:col2'])
print row['cf1:col1']
print row['cf1:col2']

Instead of providing both a column family and a column qualifier, items in the columns argument may also be just a column family, which means that all columns from that column family will be retrieved. For example, to get all columns and values in the column family cf1, use this:

row = table.row('row-key', columns=['cf1'])

In HBase, each cell has a timestamp attached to it. In case you don't want to work with the latest version of data stored in HBase, the methods that retrieve data from the database, e.g. :py:meth:`Table.row`, all accept a timestamp argument that specifies that the results should be restricted to values with a timestamp up to the specified timestamp:

row = table.row('row-key', timestamp=123456789)

By default, HappyBase does not include timestamps in the results it returns. In your application needs access to the timestamps, simply set the include_timestamp argument to True. Now, each cell in the result will be returned as a (value, timestamp) tuple instead of just a value:

row = table.row('row-key', columns=['cf1:col1'], include_timestamp=True)
value, timestamp = row['cf1:col1']

HBase supports storing multiple versions of the same cell. This can be configured for each column family. To retrieve all versions of a column for a given row, :py:meth:`Table.cells` can be used. This method returns an ordered list of cells, with the most recent version coming first. The versions argument specifies the maximum number of versions to return. Just like the methods that retrieve rows, the include_timestamp argument determines whether timestamps are included in the result. Example:

values = table.cells('row-key', 'cf1:col1', versions=2)
for value in values:
    print "Cell data: %s" % value

cells = table.cells('row-key', 'cf1:col1', versions=3, include_timestamp=True)
for value, timestamp in cells:
    print "Cell data at %d: %s" % (timestamp, value)

Note that the result may contain fewer cells than requested. The cell may just have fewer versions, or you may have requested more versions than HBase keeps for the column family.

Scanning over rows in a table

In addition to retrieving data for known row keys, rows in HBase can be efficiently iterated over using a table scanner, created using :py:meth:`Table.scan`. A basic scanner that iterates over all rows in the table looks like this:

for key, data in table.scan():
    print key, data

Doing full table scans like in the example above is prohibitively expensive in practice. Scans can be restricted in several ways to make more selective range queries. One way is to specify start or stop keys, or both. To iterate over all rows from row aaa to the end of the table:

for key, data in table.scan(row_start='aaa'):
    print key, data

To iterate over all rows from the start of the table up to row xyz, use this:

for key, data in table.scan(row_stop='xyz'):
    print key, data

To iterate over all rows between row aaa (included) and xyz (not included), supply both:

for key, data in table.scan(row_start='aaa', row_stop='xyz'):
    print key, data

An alternative is to use a key prefix. For example, to iterate over all rows starting with abc:

for key, data in table.scan(row_prefix='abc'):
    print key, data

The scanner examples above only limit the results by row key using the row_start, row_stop, and row_prefix arguments, but scanners can also limit results to certain columns, column families, and timestamps, just like :py:meth:`Table.row` and :py:meth:`Table.rows`. For advanced users, a filter string can be passed as the filter argument. Additionally, the optional limit argument defines how much data is at most retrieved, and the batch_size argument specifies how big the transferred chunks should be. The :py:meth:`Table.scan` API documentation provides more information on the supported scanner options.

Manipulating data

HBase does not have any notion of data types; all row keys, column names and column values are simply treated as raw byte strings. By design, HappyBase does not do any automatic string conversion. This means that data must be converted to byte strings in your application before you pass it to HappyBase, for instance by calling str() or by employing more advanced string serialisation techniques like struct.pack().

In HBase, all mutations either store data or mark data for deletion; there is no such thing as an in-place update or delete. HappyBase provides methods to do single inserts or deletes, and a batch API to perform multiple mutations in one go.

Storing data

To store a single cell of data in our table, we can use :py:meth:`Table.put`, which takes the row key, and the data to store. The data should be a dictionary mapping the column name to a value:

table.put('row-key', {'cf:col1': 'value1',
                      'cf:col2': 'value2'})

Use the timestamp argument if you want to provide timestamps explicitly:

table.put('row-key', {'cf:col1': 'value1'}, timestamp=123456789)

If omitted, HBase defaults to the current system time.

Deleting data

The :py:meth:`Table.delete` method deletes data from a table. To delete a complete row, just specify the row key:

table.delete('row-key')

To delete one or more columns instead of a complete row, also specify the columns argument:

table.delete('row-key', columns=['cf1:col1', 'cf1:col2'])

The optional timestamp argument restricts the delete operation to data up to the specified timestamp.

Performing batch mutations

The :py:meth:`Table.put` and :py:meth:`Table.delete` methods both issue a command to the HBase Thrift server immediately. This means that using these methods is not very efficient when storing or deleting multiple values. It is much more efficient to aggregate a bunch of commands and send them to the server in one go. This is exactly what the :py:class:`Batch` class, created using :py:meth:`Table.batch`, does. A :py:class:`Batch` instance has put and delete methods, just like the :py:class:`Table` class, but the changes are sent to the server in a single round-trip using :py:meth:`Batch.send`:

b = table.batch()
b.put('row-key-1', {'cf:col1': 'value1', 'cf:col2': 'value2'})
b.put('row-key-2', {'cf:col2': 'value2', 'cf:col3': 'value3'})
b.put('row-key-3', {'cf:col3': 'value3', 'cf:col4': 'value4'})
b.delete('row-key-4')
b.send()

Note

Storing and deleting data for the same row key in a single batch leads to unpredictable results, so don't do that.

While the methods on the :py:class:`Batch` instance resemble the :py:meth:`~Table.put` and :py:meth:`~Table.delete` methods, they do not take a timestamp argument for each mutation. Instead, you can specify a single timestamp argument for the complete batch:

b = table.batch(timestamp=123456789)
b.put(...)
b.delete(...)
b.send()

:py:class:`Batch` instances can be used as context managers, which are most useful in combination with Python's with construct. The example above can be simplified to read:

with table.batch() as b:
    b.put('row-key-1', {'cf:col1': 'value1', 'cf:col2': 'value2'})
    b.put('row-key-2', {'cf:col2': 'value2', 'cf:col3': 'value3'})
    b.put('row-key-3', {'cf:col3': 'value3', 'cf:col4': 'value4'})
    b.delete('row-key-4')

As you can see, there is no call to :py:meth:`Batch.send` anymore. The batch is automatically applied when the with code block terminates, even in case of errors somewhere in the with block, so it behaves basically the same as a try/finally clause. However, some applications require transactional behaviour, sending the batch only if no exception occurred. Without a context manager this would look something like this:

b = table.batch()
try:
    b.put('row-key-1', {'cf:col1': 'value1', 'cf:col2': 'value2'})
    b.put('row-key-2', {'cf:col2': 'value2', 'cf:col3': 'value3'})
    b.put('row-key-3', {'cf:col3': 'value3', 'cf:col4': 'value4'})
    b.delete('row-key-4')
    raise ValueError("Something went wrong!")
except ValueError as e:
    # error handling goes here; nothing is sent to HBase
    pass
else:
    # no exceptions; send data
    b.send()

Obtaining the same behaviour is easier using a with block. The transaction argument to :py:meth:`Table.batch` is all you need:

try:
    with table.batch(transaction=True) as b:
        b.put('row-key-1', {'cf:col1': 'value1', 'cf:col2': 'value2'})
        b.put('row-key-2', {'cf:col2': 'value2', 'cf:col3': 'value3'})
        b.put('row-key-3', {'cf:col3': 'value3', 'cf:col4': 'value4'})
        b.delete('row-key-4')
        raise ValueError("Something went wrong!")
except ValueError:
    # error handling goes here; nothing is sent to HBase
    pass

# when no error occurred, the transaction succeeded

As you may have imagined already, a :py:class:`Batch` keeps all mutations in memory until the batch is sent, either by calling :py:meth:`Batch.send()` explicitly, or when the with block ends. This doesn't work for applications that need to store huge amounts of data, since it may result in batches that are too big to send in one round-trip, or in batches that use too much memory. For these cases, the batch_size argument can be specified. The batch_size acts as a threshold: a :py:class:`Batch` instance automatically sends all pending mutations when there are more than batch_size pending operations. For example, this will result in three round-trips to the server (two batches with 1000 cells, and one with the remaining 400):

with table.batch(batch_size=1000) as b:
    for i in range(1200):
        # this put() will result in two mutations (two cells)
        b.put('row-%04d' % i, {'cf1:col1': 'v1',
                               'cf1:col2': 'v2',})

The appropriate batch_size is very application-specific since it depends on the data size, so just experiment to see how different sizes work for your specific use case.

Using atomic counters

The :py:meth:`Table.counter_inc` and :py:meth:`Table.counter_dec` methods allow for atomic incrementing and decrementing of 8 byte wide values, which are interpreted as big-endian 64-bit signed integers by HBase. Counters are automatically initialised to 0 upon first use. When incrementing or decrementing a counter, the value after modification is returned. Example:

print table.counter_inc('row-key', 'cf1:counter')  # prints 1
print table.counter_inc('row-key', 'cf1:counter')  # prints 2
print table.counter_inc('row-key', 'cf1:counter')  # prints 3

print table.counter_dec('row-key', 'cf1:counter')  # prints 2

The optional value argument specifies how much to increment or decrement by:

print table.counter_inc('row-key', 'cf1:counter', value=3)  # prints 5

While counters are typically used with the increment and decrement functions shown above, the :py:meth:`Table.counter_get` and :py:meth:`Table.counter_set` methods can be used to retrieve or set a counter value directly:

print table.counter_get('row-key', 'cf1:counter')  # prints 5

table.counter_set('row-key', 'cf1:counter', 12)

Note

An application should never :py:meth:`~Table.counter_get` the current value, modify it in code and then :py:meth:`~Table.counter_set` the modified value; use the atomic :py:meth:`~Table.counter_inc` and :py:meth:`~Table.counter_dec` instead!

Using the connection pool

HappyBase comes with a thread-safe connection pool that allows multiple threads to share and reuse open connections. This is most useful in multi-threaded server applications such as web applications served using Apache's mod_wsgi. When a thread asks the pool for a connection (using :py:meth:`ConnectionPool.connection`), it will be granted a lease, during which the thread has exclusive access to the connection. After the thread is done using the connection, it returns the connection to the pool so that it becomes available for other threads.

Instantiating the pool

The pool is provided by the :py:class:`ConnectionPool` class. The size argument to the constructor specifies the number of connections in the pool. Additional arguments are passed on to the :py:class:`Connection` constructor:

pool = happybase.ConnectionPool(size=3, host='...', table_prefix='myproject')

Upon instantiation, the connection pool will establish a connection immediately, so that simple problems like wrong host names are detected immediately. For the remaining connections, the pool acts lazy: new connections will be opened only when needed.

Obtaining connections

Connections can only be obtained using Python's context manager protocol, i.e. using a code block inside a with statement. This ensures that connections are actually returned to the pool after use. Example:

pool = happybase.ConnectionPool(size=3, host='...')

with pool.connection() as connection:
    print connection.tables()

Warning

Never use the connection instance after the with block has ended. Even though the variable is still in scope, the connection may have been assigned to another thread in the mean time.

Connections should be returned to the pool as quickly as possible, so that other threads can use them. This means that the amount of code included inside the with block should be kept to an absolute minimum. In practice, an application should only load data inside the with block, and process the data outside the with block:

with pool.connection() as connection:
    table = connection.table('table-name')
    row = table.row('row-key')

process_data(row)

An application thread can only hold one connection at a time. When a thread holds a connection and asks for a connection for a second time (e.g. because a called function also requests a connection from the pool), the same connection instance it already holds is returned, so this does not require any coordination from the application. This means that in the following example, both connection requests to the pool will return the exact same connection:

pool = happybase.ConnectionPool(size=3, host='...')

def do_something_else():
    with pool.connection() as connection:
        pass  # use the connection here

with pool.connection() as connection:
    # use the connection here, e.g.
    print(connection.tables())

    # call another function that uses a connection
    do_something_else()

Handling broken connections

The pool tries to detect broken connections and will replace those with fresh ones when the connection is returned to the pool. However, the connection pool does not capture raised exceptions, nor does it automatically retry failed operations. This means that the application still has to handle connection errors.

Next steps

The next step is to try it out for yourself! The :doc:`API documentation <api>` can be used as a reference.

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