# Getting Started

First, make sure you have the driver properly installed.

## Connecting to a Cluster

Before we can start executing any queries against a Cassandra cluster we need to setup an instance of `Cluster`. As the name suggests, you will typically have one instance of `Cluster` for each Cassandra cluster you want to interact with.

First, make sure you have the Cassandra driver properly installed.

### Connecting to Astra

If you are a DataStax Astra user, here is how to connect to your cluster:

1. Download the secure connect bundle from your Astra account.

2. Connect to your cluster with

    from cassandra.cluster import Cluster
    from cassandra.auth import PlainTextAuthProvider

    cloud_config = {
        'secure_connect_bundle': '/path/to/secure-connect-dbname.zip'
    }
    auth_provider = PlainTextAuthProvider(username='user', password='pass')
    cluster = Cluster(cloud=cloud_config, auth_provider=auth_provider)
    session = cluster.connect()

See Astra and Cloud for more details.

### Connecting to Cassandra

The simplest way to create a `Cluster` is like this:

    from cassandra.cluster import Cluster

    cluster = Cluster()

This will attempt to connection to a Cassandra instance on your local machine (127.0.0.1). You can also specify a list of IP addresses for nodes in your cluster:

    from cassandra.cluster import Cluster

    cluster = Cluster(['192.168.0.1', '192.168.0.2'])

The set of IP addresses we pass to the `Cluster` is simply an initial set of contact points. After the driver connects to one of these nodes it will automatically discover the rest of the nodes in the cluster and connect to them, so you don’t need to list every node in your cluster.

If you need to use a non-standard port, use SSL, or customize the driver’s behavior in some other way, this is the place to do it:

    from cassandra.cluster import Cluster
    cluster = Cluster(['192.168.0.1', '192.168.0.2'], port=..., ssl_context=...)

Instantiating a `Cluster` does not actually connect us to any nodes. To establish connections and begin executing queries we need a `Session`, which is created by calling `Cluster.connect()`:

    cluster = Cluster()
    session = cluster.connect()

## Session Keyspace

The `connect()` method takes an optional `keyspace` argument which sets the default keyspace for all queries made through that `Session`:

    cluster = Cluster()
    session = cluster.connect('mykeyspace')

You can always change a Session’s keyspace using `set_keyspace()` or by executing a `USE <keyspace>` query:

    session.set_keyspace('users')
    # or you can do this instead
    session.execute('USE users')

## Execution Profiles

Profiles are passed in by `execution_profiles` dict.

In this case we can construct the base `ExecutionProfile` passing all attributes:

    from cassandra.cluster import Cluster, ExecutionProfile, EXEC_PROFILE_DEFAULT
    from cassandra.policies import WhiteListRoundRobinPolicy, DowngradingConsistencyRetryPolicy
    from cassandra.query import tuple_factory

    profile = ExecutionProfile(
        load_balancing_policy=WhiteListRoundRobinPolicy(['127.0.0.1']),
        retry_policy=DowngradingConsistencyRetryPolicy(),
        consistency_level=ConsistencyLevel.LOCAL_QUORUM,
        serial_consistency_level=ConsistencyLevel.LOCAL_SERIAL,
        request_timeout=15,
        row_factory=tuple_factory
    )
    cluster = Cluster(execution_profiles={EXEC_PROFILE_DEFAULT: profile})
    session = cluster.connect()

    print(session.execute("SELECT release_version FROM system.local").one())

Users are free to setup additional profiles to be used by name:

    profile_long = ExecutionProfile(request_timeout=30)
    cluster = Cluster(execution_profiles={'long': profile_long})
    session = cluster.connect()
    session.execute(statement, execution_profile='long')

Also, parameters passed to `Session.execute` or attached to `Statement`s are still honored as before.

## Executing Queries

Now that we have a `Session` we can begin to execute queries. The simplest way to execute a query is to use `execute()`:

    rows = session.execute('SELECT name, age, email FROM users')
    for user_row in rows:
        print(user_row.name, user_row.age, user_row.email)

This will transparently pick a Cassandra node to execute the query against and handle any retries that are necessary if the operation fails.

By default, each row in the result set will be a namedtuple. Each row will have a matching attribute for each column defined in the schema, such as `name`, `age`, and so on. You can also treat them as normal tuples by unpacking them or accessing fields by position. The following three examples are equivalent:

    rows = session.execute('SELECT name, age, email FROM users')
    for row in rows:
        print(row.name, row.age, row.email)

    rows = session.execute('SELECT name, age, email FROM users')
    for (name, age, email) in rows:
        print(name, age, email)

    rows = session.execute('SELECT name, age, email FROM users')
    for row in rows:
        print(row[0], row[1], row[2])

If you prefer another result format, such as a `dict` per row, you can change the `row_factory` attribute.

As mentioned in our Drivers Best Practices Guide, it is highly recommended to use Prepared statements for your frequently run queries.

## Prepared Statements

Prepared statements are queries that are parsed by Cassandra and then saved for later use. When the driver uses a prepared statement, it only needs to send the values of parameters to bind. This lowers network traffic and CPU utilization within Cassandra because Cassandra does not have to re-parse the query each time.

To prepare a query, use `Session.prepare()`:

    user_lookup_stmt = session.prepare("SELECT * FROM users WHERE user_id=?")

    users = []
    for user_id in user_ids_to_query:
        user = session.execute(user_lookup_stmt, [user_id])
        users.append(user)

`prepare()` returns a `PreparedStatement` instance which can be used in place of `SimpleStatement` instances or literal string queries. It is automatically prepared against all nodes, and the driver handles re-preparing against new nodes and restarted nodes when necessary.

Note that the placeholders for prepared statements are `?` characters. This is different than for simple, non-prepared statements (although future versions of the driver may use the same placeholders for both).

Althought it is not recommended, you can also pass parameters to non-prepared statements. The driver supports two forms of parameter place-holders: positional and named.

Positional parameters are used with a `%s` placeholder. For example, when you execute:

    session.execute(
        """
        INSERT INTO users (name, credits, user_id)
        VALUES (%s, %s, %s)
        """,
        ("John O'Reilly", 42, uuid.uuid1())
    )

It is translated to the following CQL query:

    INSERT INTO users (name, credits, user_id)
    VALUES ('John O''Reilly', 42, 2644bada-852c-11e3-89fb-e0b9a54a6d93)

Note that you should use `%s` for all types of arguments, not just strings. For example, this would be wrong:

    session.execute("INSERT INTO USERS (name, age) VALUES (%s, %d)", ("bob", 42))  # wrong

Instead, use `%s` for the age placeholder.

If you need to use a literal `%` character, use `%%`.

Note: you must always use a sequence for the second argument, even if you are only passing in a single variable:

    session.execute("INSERT INTO foo (bar) VALUES (%s)", "blah")  # wrong
    session.execute("INSERT INTO foo (bar) VALUES (%s)", ("blah"))  # wrong
    session.execute("INSERT INTO foo (bar) VALUES (%s)", ("blah", ))  # right
    session.execute("INSERT INTO foo (bar) VALUES (%s)", ["blah"])  # right

Note that the second line is incorrect because in Python, single-element tuples require a comma.

Named place-holders use the `%(name)s` form:

    session.execute(
        """
        INSERT INTO users (name, credits, user_id, username)
        VALUES (%(name)s, %(credits)s, %(user_id)s, %(name)s)
        """,
        {'name': "John O'Reilly", 'credits': 42, 'user_id': uuid.uuid1()}
    )

Note that you can repeat placeholders with the same name, such as `%(name)s` in the above example.

Only data values should be supplied this way. Other items, such as keyspaces, table names, and column names should be set ahead of time (typically using normal string formatting).

For non-prepared statements, Python types are cast to CQL literals in the following way:

Python Type | CQL Literal Type
--- | ---
`None` | `NULL`
`bool` | `boolean`
`float` | `float` `double`
`int` `long` | `int` `bigint` `varint` `smallint` `tinyint` `counter`
`decimal.Decimal` | `decimal`
`str` `unicode` | `ascii` `varchar` `text`
`buffer` `bytearray` | `blob`
`date` | `date`
`datetime` | `timestamp`
`time` | `time`
`list` `tuple` generator | `list`
`set` `frozenset` | `set`
`dict` `OrderedDict` | `map`
`uuid.UUID` | `timeuuid` `uuid`

The driver supports asynchronous query execution through `execute_async()`. Instead of waiting for the query to complete and returning rows directly, this method almost immediately returns a `ResponseFuture` object. There are two ways of getting the final result from this object.

The first is by calling `result()` on it. If the query has not yet completed, this will block until it has and then return the result or raise an Exception if an error occurred. For example:

    from cassandra import ReadTimeout

    query = "SELECT * FROM users WHERE user_id=%s"
    future = session.execute_async(query, [user_id])

    # ... do some other work

    try:
        rows = future.result()
        user = rows[0]
        print(user.name, user.age)
    except ReadTimeout:
        log.exception("Query timed out:")

This works well for executing many queries concurrently:

    # build a list of futures
    futures = []
    query = "SELECT * FROM users WHERE user_id=%s"
    for user_id in ids_to_fetch:
        futures.append(session.execute_async(query, [user_id])

    # wait for them to complete and use the results
    for future in futures:
        rows = future.result()
        print(rows[0].name)

Alternatively, instead of calling `result()`, you can attach callback and errback functions through the `add_callback()`, `add_errback()`, and `add_callbacks()`, methods. If you have used Twisted Python before, this is designed to be a lightweight version of that:

    def handle_success(rows):
        user = rows[0]
        try:
            process_user(user.name, user.age, user.id)
        except Exception:
            log.error("Failed to process user %s", user.id)
            # don't re-raise errors in the callback

    def handle_error(exception):
        log.error("Failed to fetch user info: %s", exception)

    future = session.execute_async(query)
    future.add_callbacks(handle_success, handle_error)

There are a few important things to remember when working with callbacks:

  - Exceptions that are raised inside the callback functions will be logged and then ignored.

  - Your callback will be run on the event loop thread, so any long-running operations will prevent other requests from being handled

## Setting a Consistency Level

The consistency level used for a query determines how many of the replicas of the data you are interacting with need to respond for the query to be considered a success.

By default, `ConsistencyLevel.LOCAL_ONE` will be used for all queries. You can specify a different default by setting the `ExecutionProfile.consistency_level` for the execution profile with key `EXEC_PROFILE_DEFAULT`. To specify a different consistency level per request, wrap queries in a `SimpleStatement`:

    from cassandra import ConsistencyLevel
    from cassandra.query import SimpleStatement

    query = SimpleStatement(
        "INSERT INTO users (name, age) VALUES (%s, %s)",
        consistency_level=ConsistencyLevel.QUORUM)
    session.execute(query, ('John', 42))

To specify a consistency level for prepared statements, you have two options.

The first is to set a default consistency level for every execution of the prepared statement:

    from cassandra import ConsistencyLevel

    cluster = Cluster()
    session = cluster.connect("mykeyspace")
    user_lookup_stmt = session.prepare("SELECT * FROM users WHERE user_id=?")
    user_lookup_stmt.consistency_level = ConsistencyLevel.QUORUM

    # these will both use QUORUM
    user1 = session.execute(user_lookup_stmt, [user_id1])[0]
    user2 = session.execute(user_lookup_stmt, [user_id2])[0]

The second option is to create a `BoundStatement` from the `PreparedStatement` and binding parameters and set a consistency level on that:

    # override the QUORUM default
    user3_lookup = user_lookup_stmt.bind([user_id3])
    user3_lookup.consistency_level = ConsistencyLevel.ALL
    user3 = session.execute(user3_lookup)

Speculative execution is a way to minimize latency by preemptively executing several instances of the same query against different nodes. For more details about this technique, see Speculative Execution with DataStax Drivers.

To enable speculative execution:

- Configure a `SpeculativeExecutionPolicy` with the ExecutionProfile

- Mark your query as idempotent, which mean it can be applied multiple times without changing the result of the initial application. See Query Idempotence for more details.

Example:

    from cassandra.cluster import Cluster, ExecutionProfile, EXEC_PROFILE_DEFAULT
    from cassandra.policies import ConstantSpeculativeExecutionPolicy
    from cassandra.query import SimpleStatement

    # Configure the speculative execution policy
    ep = ExecutionProfile(
        speculative_execution_policy=ConstantSpeculativeExecutionPolicy(delay=.5, max_attempts=10)
    )
    cluster = Cluster(..., execution_profiles={EXEC_PROFILE_DEFAULT: ep})
    session = cluster.connect()

    # Mark the query idempotent
    query = SimpleStatement(
        "UPDATE my_table SET list_col = [1] WHERE pk = 1",
        is_idempotent=True
    )

    # Execute. A new query will be sent to the server every 0.5 second
    # until we receive a response, for a max number attempts of 10.
    session.execute(query)