This tutorial is intended as an introduction to working with Cassandra and pycassa.


Before we start, make sure that you have pycassa installed. In the Python shell, the following should run without raising an exception:

>>> import pycassa

This tutorial also assumes that a Cassandra instance is running on the default host and port. Read the instructions for getting started with Cassandra if you need help with this.

You can start Cassandra like so:

$ pwd
$ bin/cassandra -f

Creating a Keyspace and Column Families

We need to create a keyspace and some column families to work with. There are two good ways to do this: using cassandra-cli, or using pycassaShell. Both are documented below.

Using cassandra-cli

The cassandra-cli utility is included with Cassandra. It allows you to create and modify the schema, explore or modify data, and examine a few things about your cluster. Here’s how to create the keyspace and column family we need for this tutorial:

user@~ $ cassandra-cli
Welcome to cassandra CLI.

Type 'help;' or '?' for help. Type 'quit;' or 'exit;' to quit.
[default@unknown] connect localhost/9160;
Connected to: "Test Cluster" on localhost/9160
[default@unknown] create keyspace Keyspace1;
Waiting for schema agreement...
... schemas agree across the cluster
[default@unknown] use Keyspace1;
Authenticated to keyspace: Keyspace1
[default@Keyspace1] create column family ColumnFamily1;
Waiting for schema agreement...
... schemas agree across the cluster
[default@Keyspace1] quit;
user@~ $

This connects to a local instance of Cassandra and creates a keyspace named ‘Keyspace1’ with a column family named ‘ColumnFamily1’.

You can find further documentation for the CLI online.

Using pycassaShell

pycassaShell is an interactive Python shell that is included with pycassa. Upon starting, it sets up many of the objects that you typically work with when using pycassa. It provides most of the functionality that cassandra-cli does, but also gives you a full Python environment to work with.

Here’s how to create the keyspace and column family:

user@~ $ pycassaShell
Cassandra Interactive Python Shell
Keyspace: None
Host: localhost:9160

ColumnFamily instances are only available if a keyspace is specified with -k/--keyspace

Schema definition tools and cluster information are available through SYSTEM_MANAGER.
>>> SYSTEM_MANAGER.create_keyspace('Keyspace1', strategy_options={"replication_factor": "1"})
>>> SYSTEM_MANAGER.create_column_family('Keyspace1', 'ColumnFamily1')

Connecting to Cassandra

The first step when working with pycassa is to connect to the running cassandra instance:

>>> from pycassa.pool import ConnectionPool
>>> pool = ConnectionPool('Keyspace1')

The above code will connect by default to localhost:9160. We can also specify the host (or hosts) and port explicitly as follows:

>>> pool = ConnectionPool('Keyspace1', ['localhost:9160'])

This creates a small connection pool for use with a ColumnFamily . See Connection Pooling for more details.

Getting a ColumnFamily

A column family is a collection of rows and columns in Cassandra, and can be thought of as roughly the equivalent of a table in a relational database. We’ll use one of the column families that are included in the default schema file:

>>> from pycassa.pool import ConnectionPool
>>> from pycassa.columnfamily import ColumnFamily
>>> pool = ConnectionPool('Keyspace1')
>>> col_fam = ColumnFamily(pool, 'ColumnFamily1')

If you get an error about the keyspace or column family not existing, make sure you created the keyspace and column family as shown above.

Inserting Data

To insert a row into a column family we can use the insert() method:

>>> col_fam.insert('row_key', {'col_name': 'col_val'})

We can also insert more than one column at a time:

>>> col_fam.insert('row_key', {'col_name':'col_val', 'col_name2':'col_val2'})

And we can insert more than one row at a time:

>>> col_fam.batch_insert({'row1': {'name1': 'val1', 'name2': 'val2'},
...                       'row2': {'foo': 'bar'}})

Getting Data

There are many more ways to get data out of Cassandra than there are to insert data.

The simplest way to get data is to use get():

>>> col_fam.get('row_key')
{'col_name': 'col_val', 'col_name2': 'col_val2'}

Without any other arguments, get() returns every column in the row (up to column_count, which defaults to 100). If you only want a few of the columns and you know them by name, you can specify them using a columns argument:

>>> col_fam.get('row_key', columns=['col_name', 'col_name2'])
{'col_name': 'col_val', 'col_name2': 'col_val2'}

We may also get a slice (or subrange) of the columns in a row. To do this, use the column_start and column_finish parameters. One or both of these may be left empty to allow the slice to extend to one or both ends. Note that column_finish is inclusive.

>>> for i in range(1, 10):
...     col_fam.insert('row_key', {str(i): 'val'})
>>> col_fam.get('row_key', column_start='5', column_finish='7')
{'5': 'val', '6': 'val', '7': 'val'}

Sometimes you want to get columns in reverse sorted order. A common example of this is getting the last N columns from a row that represents a timeline. To do this, set column_reversed to True. If you think of the columns as being sorted from left to right, when column_reversed is True, column_start will determine the right end of the range while column_finish will determine the left.

Here’s an example of getting the last three columns in a row:

>>> col_fam.get('row_key', column_reversed=True, column_count=3)
{'9': 'val', '8': 'val', '7': 'val'}

There are a few ways to get multiple rows at the same time. The first is to specify them by name using multiget():

>>> col_fam.multiget(['row1', 'row2'])
{'row1': {'name1': 'val1', 'name2': 'val2'}, 'row_key2': {'foo': 'bar'}}

Another way is to get a range of keys at once by using get_range(). The parameter finish is also inclusive here, too. Assuming we’ve inserted some rows with keys ‘row_key1’ through ‘row_key9’, we can do this:

>>> result = col_fam.get_range(start='row_key5', finish='row_key7')
>>> for key, columns in result:
...     print key, '=>', columns
'row_key5' => {'name':'val'}
'row_key6' => {'name':'val'}
'row_key7' => {'name':'val'}


Cassandra must be using an OrderPreservingPartitioner for you to be able to get a meaningful range of rows; the default, RandomPartitioner, stores rows in the order of the MD5 hash of their keys. See http://www.datastax.com/docs/1.1/cluster_architecture/partitioning.

The last way to get multiple rows at a time is to take advantage of secondary indexes by using get_indexed_slices(), which is described in the Secondary Indexes section.

It’s also possible to specify a set of columns or a slice for multiget() and get_range() just like we did for get().


If you just want to know how many columns are in a row, you can use get_count():

>>> col_fam.get_count('row_key')

If you only want to get a count of the number of columns that are inside of a slice or have particular names, you can do that as well:

>>> col_fam.get_count('row_key', columns=['foo', 'bar'])
>>> col_fam.get_count('row_key', column_start='foo')

You can also do this in parallel for multiple rows using multiget_count():

>>> col_fam.multiget_count(['fib0', 'fib1', 'fib2', 'fib3', 'fib4'])
{'fib0': 1, 'fib1': 1, 'fib2': 2, 'fib3': 3, 'fib4': 5'}
>>> col_fam.multiget_count(['fib0', 'fib1', 'fib2', 'fib3', 'fib4'],
...                        columns=['col1', 'col2', 'col3'])
{'fib0': 1, 'fib1': 1, 'fib2': 2, 'fib3': 3, 'fib4': 3'}
>>> col_fam.multiget_count(['fib0', 'fib1', 'fib2', 'fib3', 'fib4'],
...                        column_start='col1', column_finish='col3')
{'fib0': 1, 'fib1': 1, 'fib2': 2, 'fib3': 3, 'fib4': 3'}

Typed Column Names and Values

Within a column family, column names have a specified comparator type which controls how they are sorted. Column values and row keys may also have a validation class, which validates that inserted values are the correct type.

The different types available include ASCII strings, integers, dates, UTF8, raw bytes, UUIDs, and more. See pycassa.types for a full list.

Cassandra requires you to pack column names and values into a format it can understand by using something like struct.pack(). Fortunately, when pycassa sees that a column family has a particular comparator type or validation class, it knows to pack and unpack these data types automatically for you. So, if we want to write to the StandardInt column family, which has an IntegerType comparator, we can do the following:

>>> col_fam = pycassa.ColumnFamily(pool, 'StandardInt')
>>> col_fam.insert('row_key', {42: 'some_val'})
>>> col_fam.get('row_key')
{42: 'some_val'}

Notice that 42 is an integer here, not a string.

As mentioned above, Cassandra also offers validators on column values and keys with the same set of types. Column value validators can be set for an entire column family, for individual columns, or both. pycassa knows to pack these column values automatically too. Suppose we have a Users column family with two columns, name and age, with types UTF8Type and IntegerType:

>>> col_fam = pycassa.ColumnFamily(pool, 'Users')
>>> col_fam.insert('thobbs', {'name': 'Tyler', 'age': 24})
>>> col_fam.get('thobbs')
{'name': 'Tyler', 'age': 24}

Of course, if pycassa‘s automatic behavior isn’t working for you, you can turn it off or change it using autopack_names, autopack_values, column_name_class, default_validation_class, and so on.

Connection Pooling

Pycassa uses connection pools to maintain connections to Cassandra servers. The ConnectionPool class is used to create the connection pool. After creating the pool, it may be used to create multiple ColumnFamily objects.

>>> pool = pycassa.ConnectionPool('Keyspace1', pool_size=20)
>>> standard_cf = pycassa.ColumnFamily(pool, 'Standard1')
>>> standard_cf.insert('key', {'col': 'val'})
>>> super_cf = pycassa.ColumnFamily(pool, 'Super1')
>>> super_cf.insert('key2', {'column' : {'col': 'val'}})
>>> standard_cf.get('key')
{'col': 'val'}
>>> pool.dispose()

Automatic retries (or “failover”) happen by default with ConectionPools. This means that if any operation fails, it will be transparently retried on other servers until it succeeds or a maximum number of failures is reached.