Package: bytewax
Python Stateful Stream Processing FrameworkBytewax is a Python framework that simplifies event and stream processing. Because Bytewax couples the stream and event processing capabilities of Flink, Spark, and Kafka Streams with the friendly and familiar interface of Python, you can re-use the Python libraries you already know and love. Connect data sources, run stateful transformations and write to various different downstream systems with built-in connectors or existing Python libraries.How it all worksBytewax is a Python framework and Rust distributed processing engine that uses a dataflow computational model to provide parallelizable stream processing and event processing capabilities similar to Flink, Spark, and Kafka Streams. You can use Bytewax for a variety of workloads from moving data à la Kafka Connect style all the way to advanced online machine learning workloads. Bytewax is not limited to streaming applications but excels anywhere that data can be distributed at the input and output.Bytewax has an accompanying command line interface,waxctl, which supports the deployment of dataflows on cloud servers or Kubernetes. You can download ithere.Getting Started with BytewaxpipinstallbytewaxInstall waxctlDataflow, Input and OperatorsA Bytewax dataflow is Python code that will represent an input, a series of processing steps, and an output. The inputs could range from a Kafka stream to a WebSocket and the outputs could vary from a data lake to a key-value store.importjsonfrombytewaximportoperatorsasopfrombytewax.connectors.kafkaimportoperatorsaskopfrombytewax.dataflowimportDataflow# Bytewax has input and output helpers for common input and output data sources# but you can also create your own with the [Sink and Source API](https://github.com/bytewax/bytewax/blob/main/docs/articles/advanced-concepts/custom-io-connectors.md).At a high-level, the dataflow compute model is one in which a program execution is conceptualized as data flowing through a series of operator-based steps. Operators likemapandfilterare the processing primitives of Bytewax. Each of them gives you a “shape” of data transformation, and you give them regular Python functions to customize them to a specific task you need. See the documentation for a list of theavailable operatorsBROKERS=["localhost:19092"]IN_TOPICS=["in_topic"]OUT_TOPIC="out_topic"ERR_TOPIC="errors"defdeserialize(kafka_message):returnjson.loads(kafka_message.value)defanonymize_email(event_data):event_data["email"]="@".join(["******",event_data["email"].split("@")[-1]])returnevent_datadefremove_bytewax(event_data):return"bytewax"notinevent_data["email"]flow=Dataflow("kafka_in_out")stream=kop.input("inp",flow,brokers=BROKERS,topics=IN_TOPICS)# we can inspect the stream coming from the kafka topic to view the items within on std out for debuggingop.inspect("inspect-oks",stream.oks)# we can also inspect kafka errors as a separate stream and raise an exception when one is encounterederrs=op.inspect("errors",stream.errs).then(op.raises,"crash-on-err")deser_msgs=op.map("deserialize",stream.oks,deserialize)anon_msgs=op.map("anon",deser_msgs,anonymize_email)filtered_msgs=op.filter("filter_employees",anon_msgs,remove_bytewax)processed=op.map("map",anon_msgs,lambdam:KafkaSinkMessage(None,json.dumps(m)))# and finally output the cleaned data to a new topickop.output("out1",processed,brokers=BROKERS,topic=OUT_TOPIC)Windowing, Reducing and AggregatingBytewax is a stateful stream processing framework, which means that some operations remember information across multiple events.  Windows and aggregations are also stateful, and can be reconstructed in the event of failure. Bytewax can be configured with differentstate recovery mechanismsto durably persist state in order to recover from failure.There are multiple stateful operators available likereduce,stateful_mapandfold_window. The complete list can be found in theAPI documentation for all operators. Below we use thefold_windowoperator with a tumbling window based on system time to gather events and calculate the number of times events have occurred on a per-user basis.fromdatetimeimportdatetime,timedelta,timezonefrombytewax.dataflowimportDataflowfrombytewax.operatorsimportwindowaswindow_opfrombytewax.operators.windowimportEventClockConfig,TumblingWindow# This is the accumulator function, and outputs a list of 2-tuples,# containing the event's "value" and it's "time" (used later to print info)defacc_values(acc,event):acc.append((event["value"],event["time"]))returnacc# This function instructs the event clock on how to retrieve the# event's datetime from the input.# Note that the datetime MUST be UTC. If the datetime is using a different# representation, we would have to convert it here.defget_event_time(event):returndatetime.fromisoformat(event["time"])# Configure the `fold_window` operator to use the event time.cc=EventClockConfig(get_event_time,wait_for_system_duration=timedelta(seconds=10))# And a 5 seconds tumbling windowalign_to=datetime(2023,1,1,tzinfo=timezone.utc)wc=TumblingWindow(align_to=align_to,length=timedelta(seconds=5))running_avg_stream=window_op.fold_window("running_average",keyed_stream,cc,wc,list,acc_values)Merges and JoinsMerging or Joining multiple input streams is a common task for stream processing, Bytewax enables different types of joins to facilitate different patters.Merging StreamsMerging streams is like concatenating, there is no logic and the resulting stream will potentially include heterogenous records.frombytewaximportoperatorsasopfrombytewax.connectors.stdioimportStdOutSinkfrombytewax.dataflowimportDataflowfrombytewax.testingimportTestingSourceflow=Dataflow("merge")src_1=[{"user_id":"123","name":"Bumble"},]inp1=op.input("inp1",flow,TestingSource(src_1))src_2=[{"user_id":"123","email":"bee@bytewax.com"},{"user_id":"456","email":"hive@bytewax.com"},]inp2=op.input("inp2",flow,TestingSource(src_2))merged_stream=op.merge("merge",inp1,inp2)op.inspect("debug",merged_stream)Joining StreamsJoining streams is different than merging because it uses logic to join the records in the streams together. The joins in Bytewax can be running or not. A regular join in streaming is more closely related to an inner join in SQL in that the dataflow will emit data downstream from a join when all of the sides of the join have matched on the key.frombytewaximportoperatorsasopfrombytewax.connectors.stdioimportStdOutSinkfrombytewax.dataflowimportDataflowfrombytewax.testingimportTestingSourceflow=Dataflow("join")src_1=[{"user_id":"123","name":"Bumble"},]inp1=op.input("inp1",flow,TestingSource(src_1))keyed_inp_1=op.key_on("key_stream_1",inp1,lambdax:x["user_id"])src_2=[{"user_id":"123","email":"bee@bytewax.com"},{"user_id":"456","email":"hive@bytewax.com"},]inp2=op.input("inp2",flow,TestingSource(src_2))keyed_inp_2=op.key_on("key_stream_2",inp2,lambdax:x["user_id"])merged_stream=op.join("join",keyed_inp_1,keyed_inp_2)op.inspect("debug",merged_stream)OutputOutput in Bytewax is described as a sink and these are grouped intoconnectors. There are a number of basic connectors in the bytewax repo to help you during development. In addition to the built-in connectors, it is possible to use the input and output API to build a custom sink and source. There is also a hub for connectors built by the community, partners and Bytewax. Below is an example of a custom connector for Postgres using the psycopg2 library.importpsycopg2frombytewaximportoperatorsasopfrombytewax.outputsimportFixedPartitionedSink,StatefulSinkPartitionclassPsqlSink(StatefulSinkPartition):def__init__(self):self.conn=psycopg2.connect("dbname=website user=bytewax")self.conn.set_session(autocommit=True)self.cur=self.conn.cursor()defwrite_batch(self,values):query_string="""INSERT INTO events (user_id, data)VALUES (%s,%s)ON CONFLICT (user_id)DO UPDATE SET data =%s;"""self.cur.execute_values(query_string,values)defsnapshot(self):passdefclose(self):self.conn.close()classPsqlOutput(FixedPartitionedSink):deflist_parts(self):return["single"]defbuild_part(for_part,resume_state):returnPsqlSink()ExecutionBytewax dataflows can be executed in a single Python process, or on multiple processes on multiple hosts with multiple worker threads. When processing data in a distributed fashion, Bytewax uses routing keys to ensure your state is updated in a correct way automatically.# a single worker locallypython-mbytewax.runmy_dataflow:flow# Start two worker threads in a single process.python-mbytewax.runmy_dataflow-w2# Start a process on two separate machines to form a Bytewax cluster.# Start the first process with two worker threads on `machine_one`.machine_one$python-mbytewax.runmy_dataflow-w2-i0-a"machine_one:2101;machine_two:2101"# Then start the second process with three worker threads on `machine_two`.machine_two$python-mbytewax.runmy_dataflow-w3-i1-a"machine_one:2101;machine_two:2101"It can also be run in a Docker container as described further in thedocumentation.KubernetesThe recommended way to run dataflows at scale is to leverage thekubernetes ecosystem. To help manage deployment, we builtwaxctl, which allows you to easily deploy dataflows that will run at huge scale across multiple compute nodes.waxctldfdeploymy_dataflow.py--namemy-dataflowWhy Bytewax?At a high level, Bytewax provides a few major benefits:The operators in Bytewax are largely “data-parallel”, meaning they can operate on independent parts of the data concurrently.Bytewax offers the ability to express higher-level control constructs, like iteration.Bytewax allows you to develop and run your code locally, and then easily scale that code to multiple workers or processes without changes.Bytewax can be used in both a streaming and batch contextAbility to leverage the Python ecosystem directlyCommunitySlackIs the main forum for communication and discussion.GitHub Issuesis reserved only for actual issues. Please use the slack community for discussions.Code of ConductUsageInstall thelatest releasewith pip:pipinstallbytewaxBuilding From SourceTo build a specific branch, you will need to use Maturin and have Rust installed on your machine. Once those have been installed runmaturindevelop-EdevImportant: If you are testing with a maturin built version from source, you should usematurin build --releasesincematurin developwill be slower.More ExamplesFor a more complete example, and documentation on the available operators, check out theUser Guideand the/examplesfolder.LicenseBytewax is licensed under theApache-2.0license.ContributingContributions are welcome! This community and project would not be what it is without thecontributors. All contributions, from bug reports to new features, are welcome and encouraged. Please view thecontribution guidelinesbefore getting started.With ❤️ Bytewax