Welcome to sparkly’s documentation!

Sparkly is a library that makes usage of pyspark more convenient and consistent.

A brief tour on Sparkly features:

# The main entry point is SparklySession,
# you can think of it as of a combination of SparkSession and SparkSession.builder.
from sparkly import SparklySession


# Define dependencies in the code instead of messing with `spark-submit`.
class MySession(SparklySession):
    # Spark packages and dependencies from Maven.
    packages = [
        'datastax:spark-cassandra-connector:2.0.0-M2-s_2.11',
        'mysql:mysql-connector-java:5.1.39',
    ]

    # Jars and Hive UDFs
    jars = ['/path/to/brickhouse-0.7.1.jar'],
    udfs = {
        'collect_max': 'brickhouse.udf.collect.CollectMaxUDAF',
    }


spark = MySession()

# Operate with interchangeable URL-like data source definitions:
df = spark.read_ext.by_url('mysql://<my-sql.host>/my_database/my_database')
df.write_ext('parquet:s3://<my-bucket>/<path>/data?partition_by=<field_name1>')

# Interact with Hive Metastore via convenient python api,
# instead of verbose SQL queries:
spark.catalog_ext.has_table('my_custom_table')
spark.catalog_ext.get_table_properties('my_custom_table')

# Easy integration testing with Fixtures and base test classes.
from pyspark.sql import types as T
from sparkly.testing import SparklyTest


class TestMyShinySparkScript(SparklyTest):
    session = MySession

    fixtures = [
        MysqlFixture('<my-testing-host>', '<test-user>', '<test-pass>', '/path/to/data.sql', '/path/to/clear.sql')
    ]

   def test_job_works_with_mysql(self):
      df = self.spark.read_ext.by_url('mysql://<my-testing-host>/<test-db>/<test-table>?user=<test-usre>&password=<test-password>')
      res_df = my_shiny_script(df)
      self.assertRowsEqual(
         res_df.collect(),
         [T.Row(fieldA='DataA', fieldB='DataB', fieldC='DataC')],
      )

Sparkly Session

SparklySession is the main entry point to sparkly’s functionality. It’s derived from SparkSession to provide additional features on top of the default session. The are two main differences between SparkSession and SparklySession:

1. SparklySession doesn’t have builder attribute, because we prefer declarative session definition over imperative.

2. Hive support is enabled by default.

The example below shows both imperative and declarative approaches:

# PySpark-style (imperative)
from pyspark import SparkSession

spark = SparkSession.builder\
    .appName('My App')\
    .master('spark://')\
    .config('spark.sql.shuffle.partitions', 10)\
    .getOrCreate()

# Sparkly-style (declarative)
from sparkly import SparklySession

class MySession(SparklySession):
    options = {
        'spark.app.name': 'My App',
        'spark.master': 'spark://',
        'spark.sql.shuffle.partitions': 10,
    }

spark = MySession()

# In case you want to change default options
spark = MySession({'spark.app.name': 'My Awesome App'})

# In case you want to access the session singleton
spark = MySession.get_or_create()

Installing dependencies

Why: Spark forces you to specify dependencies (spark packages or maven artifacts) when a spark job is submitted (something like spark-submit --packages=...). We prefer a code-first approach where dependencies are actually declared as part of the job.

For example: You want to read data from Cassandra.

from sparkly import SparklySession


class MySession(SparklySession):
    # Define a list of spark packages or maven artifacts.
    packages = [
        'datastax:spark-cassandra-connector:2.0.0-M2-s_2.11',
    ]

# Dependencies will be fetched during the session initialisation.
spark = MySession()

# Here is how you now can access a dataset in Cassandra.
df = spark.read_ext.by_url('cassandra://<cassandra-host>/<db>/<table>?consistency=QUORUM')

Custom Maven repositories

Why: If you have a private maven repository, this is how to point spark to it when it performs a package lookup. Order in which dependencies will be resolved is next:

• Local cache

• Custom maven repositories (if specified)

• Maven Central

For example: Let’s assume your maven repository is available on: http://my.repo.net/maven, and there is some spark package published there, with identifier: my.corp:spark-handy-util:0.0.1 You can install it to a spark session like this:

from sparkly import SparklySession

class MySession(SparklySession):
    repositories = ['http://my.repo.net/maven']
    packages = ['my.corp:spark-handy-util:0.0.1']

spark = MySession()

Tuning options

Why: You want to customise your spark session.

For example:

• spark.sql.shuffle.partitions to tune shuffling;

• hive.metastore.uris to connect to your own HiveMetastore;

• spark.hadoop.avro.mapred.ignore.inputs.without.extension package specific options.

from sparkly import SparklySession


class MySession(SparklySession):
    options = {
        # Increase the default amount of partitions for shuffling.
        'spark.sql.shuffle.partitions': 1000,
        # Setup remote Hive Metastore.
        'hive.metastore.uris': 'thrift://<host1>:9083,thrift://<host2>:9083',
        # Ignore files without `avro` extensions.
        'spark.hadoop.avro.mapred.ignore.inputs.without.extension': 'false',
    }

# You can also overwrite or add some options at initialisation time.
spark = MySession({'spark.sql.shuffle.partitions': 10})

Tuning options through shell environment

Why: You want to customize your spark session in a way that depends on the hardware specifications of your worker (or driver) machine(s), so you’d rather define them close to where the actual machine specs are requested / defined. Or you just want to test some new configuration without having to change your code. In both cases, you can do so by using the environmental variable PYSPARK_SUBMIT_ARGS. Note that any options defined this way will override any conflicting options from your Python code.

For example:

• spark.executor.cores to tune the cores used by each executor;

• spark.executor.memory to tune the memory available to each executor.

PYSPARK_SUBMIT_ARGS='--conf "spark.executor.cores=32" --conf "spark.executor.memory=160g"' \
    ./my_spark_app.py

Using UDFs

Why: To start using Java UDF you have to import JAR file via SQL query like add jar ../path/to/file and then call registerJavaFunction. We think it’s too many actions for such simple functionality.

For example: You want to import UDFs from brickhouse library.

from pyspark.sql.types import IntegerType
from sparkly import SparklySession


def my_own_udf(item):
    return len(item)


class MySession(SparklySession):
    # Import local jar files.
    jars = [
        '/path/to/brickhouse.jar'
    ]
    # Define UDFs.
    udfs = {
        'collect_max': 'brickhouse.udf.collect.CollectMaxUDAF',  # Java UDF.
        'my_udf': (my_own_udf, IntegerType()),  # Python UDF.
    }

spark = MySession()

spark.sql('SELECT collect_max(amount) FROM my_data GROUP BY ...')
spark.sql('SELECT my_udf(amount) FROM my_data')

Lazy access / initialization

Why: A lot of times you might need access to the sparkly session at a low-level, deeply nested function in your code. A first approach is to declare a global sparkly session instance that you access explicitly, but this usually makes testing painful because of unexpected importing side effects. A second approach is to pass the session instance explicitly as a function argument, but this makes the code ugly since you then need to propagate that argument all the way up to every caller of that function.

Other times you might want to be able to glue together and run one after the other different code segments, where each segment initializes its own sparkly session, despite the sessions being identical. This situation could occur when you are doing investigative work in a notebook.

In both cases, SparklySession.get_or_create is the answer, as it solves the problems mentioned above while keeping your code clean and tidy.

For example: You want to use a read function within a transformation.

from sparkly import SparklySession


class MySession(SparklySession):
    pass

def my_awesome_transformation():
    df = read_dataset('parquet:s3://path/to/my/data')
    df2 = read_dataset('parquet:s3://path/to/my/other/data')
    # do something with df and df2...

def read_dataset(url):
    spark = MySession.get_or_create()
    return spark.read_ext.by_url(url)

API documentation

Read/write utilities for DataFrames

Sparkly isn’t trying to replace any of existing storage connectors. The goal is to provide a simplified and consistent api across a wide array of storage connectors. We also added the way to work with abstract data sources, so you can keep your code agnostic to the storages you use.

Cassandra

Sparkly relies on the official spark cassandra connector and was successfully tested in production using version 2.4.0.

Package	https://spark-packages.org/package/datastax/spark-cassandra-connector
Configuration	https://github.com/datastax/spark-cassandra-connector/blob/v2.4.0/doc/reference.md

For using overwrite mode, it is needed to specify confirm.truncate as true. Otherwise, use append mode to update existing data.

from sparkly import SparklySession


class MySession(SparklySession):
    # Feel free to play with other versions
    packages = ['datastax:spark-cassandra-connector:2.4.0-s_2.11']

spark = MySession()

# To read data
df = spark.read_ext.cassandra('localhost', 'my_keyspace', 'my_table')
# To write data
df.write_ext.cassandra('localhost', 'my_keyspace', 'my_table')

Elastic

Sparkly relies on the official elastic spark connector and was successfully tested in production using version 6.5.4.

Package	https://spark-packages.org/package/elastic/elasticsearch-hadoop
Configuration	https://www.elastic.co/guide/en/elasticsearch/hadoop/7.3/configuration.html

from sparkly import SparklySession


class MySession(SparklySession):
    # Feel free to play with other versions
    packages = ['org.elasticsearch:elasticsearch-spark-20_2.11:7.3.0']

spark = MySession()

# To read data
df = spark.read_ext.elastic('localhost', 'my_index', 'my_type', query='?q=awesomeness')
# To write data
df.write_ext.elastic('localhost', 'my_index', 'my_type')

Kafka

Sparkly’s reader and writer for Kafka are built on top of the official spark package for Kafka-SQL.

Package	https://mvnrepository.com/artifact/org.apache.spark/spark-sql-kafka-0-10_2.11/2.4.0
Configuration	https://spark.apache.org/docs/2.4.0/structured-streaming-kafka-integration.html

import json

from sparkly import SparklySession


class MySession(SparklySession):
    packages = [
        'org.apache.spark:spark-sql-kafka-0-10_2.11:2.4.0',
    ]

spark = MySession()

# To read JSON messaged from Kafka into a dataframe:

#   1. Define a schema of the messages you read.
df_schema = StructType([
    StructField('key', StructType([
        StructField('id', StringType(), True)
    ])),
    StructField('value', StructType([
        StructField('name', StringType(), True),
        StructField('surname', StringType(), True),
    ]))
])

#   2. Specify the schema as a reader parameter.
df = hc.read_ext.kafka(
    'kafka.host',
    topic='my.topic',
    # key & value deserialization is optional; if not provided,
    # then the user will have to deal with decoding the binary directly.
    key_deserializer=lambda item: json.loads(item.decode('utf-8')),
    value_deserializer=lambda item: json.loads(item.decode('utf-8')),
    # if deserializers are used, the schema must be provided:
    schema=df_schema,
)

# To write a dataframe to Kafka in JSON format:
df.write_ext.kafka(
    'kafka.host',
    topic='my.topic',
    # key & value serialization is optional; if not provided,
    # the `key` and `value` columns MUST already be StringType or BinaryType
    key_serializer=lambda item: json.dumps(item).encode('utf-8'),
    value_serializer=lambda item: json.dumps(item).encode('utf-8'),
)

MySQL

Basically, it’s just a high level api on top of the native jdbc reader and jdbc writer.

Jars	https://mvnrepository.com/artifact/mysql/mysql-connector-java
Configuration	https://dev.mysql.com/doc/connector-j/5.1/en/connector-j-reference-configuration-properties.html

from sparkly import SparklySession
from sparkly.utils import absolute_path


class MySession(SparklySession):
    # Feel free to play with other versions.
    packages = ['mysql:mysql-connector-java:6.0.6']


spark = MySession()

# To read data
df = spark.read_ext.mysql('localhost', 'my_database', 'my_table',
                          options={'user': 'root', 'password': 'root'})
# To write data
df.write_ext.mysql('localhost', 'my_database', 'my_table', options={
    'user': 'root',
    'password': 'root',
    'rewriteBatchedStatements': 'true',  # improves write throughput dramatically
})

Redis

Sparkly provides a writer for Redis that is built on top of the official redis python library redis-py . It is currently capable of exporting your DataFrame as a JSON blob per row or group of rows.

Note

• To interact with Redis, sparkly needs the redis library. You can get it via: pip install sparkly[redis]

import json

from sparkly import SparklySession


spark = SparklySession()

# Write JSON.gz data indexed by col1.col2 that will expire in a day
df.write_ext.redis(
    host='localhost',
    port=6379,
    key_by=['col1', 'col2'],
    exclude_key_columns=True,
    expire=24 * 60 * 60,
    compression='gzip',
)

Universal reader/writer

The DataFrame abstraction is really powerful when it comes to transformations. You can shape your data from various storages using exactly the same api. For instance, you can join data from Cassandra with data from Elasticsearch and write the result to MySQL.

The only problem - you have to explicitly define sources (or destinations) in order to create (or export) a DataFrame. But the source/destination of data doesn’t really change the logic of transformations (if the schema is preserved). To solve the problem, we decided to add the universal api to read/write DataFrames:

from sparkly import SparklyContext

class MyContext(SparklyContext):
    packages = [
        'datastax:spark-cassandra-connector:1.6.1-s_2.10',
        'com.databricks:spark-csv_2.10:1.4.0',
        'org.elasticsearch:elasticsearch-spark_2.10:6.5.4',
    ]

hc = MyContext()

# To read data
df = hc.read_ext.by_url('cassandra://localhost/my_keyspace/my_table?consistency=ONE')
df = hc.read_ext.by_url('csv:s3://my-bucket/my-data?header=true')
df = hc.read_ext.by_url('elastic://localhost/my_index/my_type?q=awesomeness')
df = hc.read_ext.by_url('parquet:hdfs://my.name.node/path/on/hdfs')

# To write data
df.write_ext.by_url('cassandra://localhost/my_keyspace/my_table?consistency=QUORUM&parallelism=8')
df.write_ext.by_url('csv:hdfs://my.name.node/path/on/hdfs')
df.write_ext.by_url('elastic://localhost/my_index/my_type?parallelism=4')
df.write_ext.by_url('parquet:s3://my-bucket/my-data?header=false')

Controlling the load

From the official documentation:

Don’t create too many partitions in parallel on a large cluster; otherwise Spark might crash your external database systems.

link: <https://spark.apache.org/docs/2.0.1/api/java/org/apache/spark/sql/DataFrameReader.html>

It’s a very good advice, but in practice it’s hard to track the number of partitions. For instance, if you write a result of a join operation to database the number of splits might be changed implicitly via spark.sql.shuffle.partitions.

To prevent us from shooting to the foot, we decided to add parallelism option for all our readers and writers. The option is designed to control a load on a source we write to / read from. It’s especially useful when you are working with data storages like Cassandra, MySQL or Elastic. However, the implementation of the throttling has some drawbacks and you should be aware of them.

The way we implemented it is pretty simple: we use coalesce on a dataframe to reduce an amount of tasks that will be executed in parallel. Let’s say you have a dataframe with 1000 splits and you want to write no more than 10 task in parallel. In such case coalesce will create a dataframe that has 10 splits with 100 original tasks in each. An outcome of this: if any of these 100 tasks fails, we have to retry the whole pack in 100 tasks.

Read more about coalesce

Reader API documentation

Writer API documentation

Hive Metastore Utils

About Hive Metastore

The Hive Metastore is a database with metadata for Hive tables.

To configure SparklySession to work with external Hive Metastore, you need to set hive.metastore.uris option. You can do this via hive-site.xml file in spark config ($SPARK_HOME/conf/hive-site.xml):

<property>
  <name>hive.metastore.uris</name>
  <value>thrift://<n.n.n.n>:9083</value>
  <description>IP address (or fully-qualified domain name) and port of the metastore host</description>
</property>

or set it dynamically via SparklySession options:

class MySession(SparklySession):
    options = {
        'hive.metastore.uris': 'thrift://<n.n.n.n>:9083',
    }

Tables management

Why: you need to check if tables exist, rename them, drop them, or even overwrite existing aliases in your catalog.

from sparkly import SparklySession


spark = SparklySession()

assert spark.catalog_ext.has_table('my_table') in {True, False}
spark.catalog_ext.rename_table('my_table', 'my_new_table')
spark.catalog_ext.create_table('my_new_table', path='s3://my/parquet/data', source='parquet', mode='overwrite')
spark.catalog_ext.drop_table('my_new_table')

Table properties management

Why: sometimes you want to assign custom attributes for your table, e.g. creation time, last update, purpose, data source. The only way to interact with table properties in spark - use raw SQL queries. We implemented a more convenient interface to make your code cleaner.

from sparkly import SparklySession


spark = SparklySession()
spark.catalog_ext.set_table_property('my_table', 'foo', 'bar')
assert spark.catalog_ext.get_table_property('my_table', 'foo') == 'bar'
assert spark.catalog_ext.get_table_properties('my_table') == {'foo': 'bar'}

Note properties are stored as strings. In case if you need other types, consider using a serialisation format, e.g. JSON.

Using non-default database

Why to split your warehouse into logical groups (for example by system components). In all catalog_ext.* methods you can specify full table names <db-name>.<table-name> and it should operate properly

from time import time
from sparkly import SparklySession

spark = SparklySession()

if spark.catalog_ext.has_database('my_database'):
    self.catalog_ext.rename_table(
        'my_database.my_badly_named_table',
        'new_shiny_name',
    )
    self.catalog_ext.set_table_property(
        'my_database.new_shiny_name',
        'last_update_at',
        time(),
    )

Note be careful using ‘USE’ statements like: spark.sql(‘USE my_database’), it’s stateful and may lead to weird errors, if code assumes correct current database.

API documentation

Testing Utils

Base TestCases

There are two main test cases available in Sparkly:

• SparklyTest creates a new session for each test case.

• SparklyGlobalSessionTest uses a single sparkly session for all test cases to boost performance.

from pyspark.sql import types as T

from sparkly import SparklySession
from sparkly.testing import SparklyTest, SparklyGlobalSessionTest


class MyTestCase(SparklyTest):
    session = SparklySession

    def test(self):
        df = self.spark.read_ext.by_url(...)

        # Compare all fields
        self.assertRowsEqual(
            df.collect(),
            [
                T.Row(col1='row1', col2=1),
                T.Row(col1='row2', col2=2),
            ],
        )

...

class MyTestWithReusableSession(SparklyGlobalSessionTest):
    context = SparklySession

    def test(self):
        df = self.spark.read_ext.by_url(...)

...

DataFrame Assertions

Asserting that the dataframe produced by your transformation is equal to some expected output can be unnecessarily complicated at times. Common issues include:

• Ignoring the order in which elements appear in an array. This could be particularly useful when that array is generated as part of a groupBy aggregation, and you only care about all elements being part of the end result, rather than the order in which Spark encountered them.

• Comparing floats that could be arbitrarily nested in complicated datatypes within a given tolerance; exact matching is either fragile or impossible.

• Ignoring whether a field of a complex datatype is nullable. Spark infers this based on the applied transformations, but it is oftentimes inaccurate. As a result, assertions on complex data types might fail, even though in theory they shouldn’t have.

• Having rows with different field names compare equal if the values match in alphabetical order of the names (see unit tests for example).

• Unhelpful diffs in case of mismatches.

Sparkly addresses these issues by providing assertRowsEqual:

from pyspark.sql import types as T

from sparkly import SparklySession
from sparkly.test import SparklyTest


def my_transformation(spark):
    return spark.createDataFrame(
        data=[
            ('row1', {'field': 'value_1'}, [1.1, 2.2, 3.3]),
            ('row2', {'field': 'value_2'}, [4.1, 5.2, 6.3]),
        ],
        schema=T.StructType([
            T.StructField('id', T.StringType()),
            T.StructField(
                'st',
                T.StructType([
                    T.StructField('field', T.StringType()),
                ]),
            ),
            T.StructField('ar', T.ArrayType(T.FloatType())),
        ]),
    )


class MyTestCase(SparklyTest):
    session = SparklySession

    def test(self):
        df = my_transformation(self.spark)

        self.assertRowsEqual(
            df.collect(),
            [
                T.Row(id='row2', st=T.Row(field='value_2'), ar=[6.0, 5.0, 4.0]),
                T.Row(id='row1', st=T.Row(field='value_1'), ar=[2.0, 3.0, 1.0]),
            ],
            atol=0.5,
        )

Instant Iterative Development

The slowest part in Spark integration testing is context initialisation. SparklyGlobalSessionTest allows you to keep the same instance of spark context between different test cases, but it still kills the context at the end. It’s especially annoying if you work in TDD fashion. On each run you have to wait 25-30 seconds till a new context is ready. We added a tool to preserve spark context between multiple test runs.

# Activate instant testing mode.
sparkly-testing up

# The first run is slow (context is created).
py.test tests/my_integration_test_with_sparkly.py

# The second run and all after it are fast (context is reused).
py.test tests/my_integration_test_with_sparkly.py

# Deactivate instant testing mode (when you are done with testing).
sparkly-testing down

Note

In case if you change SparklySession definition (new options, jars or packages) you have to refresh the context via sparkly-testing refresh. However, you don’t need to refresh context if udfs are changed.

Fixtures

“Fixture” is a term borrowed from Django framework. Fixtures load data to a database before the test execution.

There are several storages supported in Sparkly:

• Elastic

• Cassandra (requires cassandra-driver)

• Mysql (requires PyMySql)

• Kafka (requires kafka-python)

from sparkly.test import MysqlFixture, SparklyTest


class MyTestCase(SparklyTest):
    ...
    fixtures = [
        MysqlFixture('mysql.host',
                     'user',
                     'password',
                     '/path/to/setup_data.sql',
                     '/path/to/remove_data.sql')
    ]
    ...

Column and DataFrame Functions

A counterpart of pyspark.sql.functions providing useful shortcuts:

• a cleaner alternative to chaining together multiple when/otherwise statements.

• an easy way to join multiple dataframes at once and disambiguate fields with the same name.

• agg function to select a value from the row that maximizes other column(s)

API documentation

Generic Utils

These are generic utils used in Sparkly.

License

                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

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=======================================================================
Sparkly Subcomponents:

The Sparkly project contains subcomponents with separate copyright
notices and license terms. Your use of the source code for the these
subcomponents is subject to the terms and conditions of the following
licenses.

========================================================================
Apache licenses
========================================================================

The following dependencies are provided under a Apache license. See project link for details.

(Apache License 2.0) Spark (https://github.com/apache/spark)
(Apache License 2.0) cassandra-driver (https://github.com/datastax/python-driver)

========================================================================
BSD-style licenses
========================================================================

The following dependencies are provided under a BSD-style license. See project link for details.

(BSD License) mock (https://github.com/testing-cabal/mock)
(PSF License) Sphinx (https://github.com/sphinx-doc/sphinx)

========================================================================
MIT licenses
========================================================================

The following dependencies are provided under the MIT License. See project link for details.

(MIT License) sphinx_rtd_theme (https://github.com/snide/sphinx_rtd_theme)
(MIT License) pytest (https://github.com/pytest-dev/pytest)
(MIT License) pytest-cov (https://github.com/pytest-dev/pytest-cov)
(MIT License) PyMySQL (https://github.com/PyMySQL/PyMySQL)

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