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Beating Python's GIL! to Max Out Your CPUs Andrew Montalenti! CTO, Parse.ly @amontalenti

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Scaling Python! to 3,000 Cores Andrew Montalenti! CTO, Parse.ly @amontalenti OR:

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What happens when you have 153 TB of compressed customer data that may need to be reprocessed at any time, and it’s now growing at 10-20TB per month?

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@dabeaz = “the GIL guy”

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Is the GIL a feature, not a bug?! In one Python process, at any one time, only one Python bytecode instruction is executing at once.

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should we just rewrite it in Go?

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fast functions! running in parallel

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Python State Code Server 1 Core 2 Core 1 Server 2 Core 2 Core 1 Server 3 Core 2 Core 1 from urllib.parse import urlparse urls = ["http://arstechnica.com/", "http://ars.to/1234", "http://ars.to/5678", ...]

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Python State Code Server 1 Core 2 Core 1 Server 2 Core 2 Core 1 Server 3 Core 2 Core 1 map(urlparse, urls) from urllib.parse import urlparse urls = ["http://arstechnica.com/", "http://ars.to/1234", "http://ars.to/5678", ...]

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Cython speeding up functions on a single core

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concurrent.futures good map API, but odd implementation details

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Python State Code Server 1 Core 2 Core 1 Server 2 Core 2 Core 1 Server 3 Core 2 Core 1 executor = ThreadPoolExecutor() executor.map(urlparse, urls)

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Python State Code Server 1 Core 2 Core 1 Server 2 Core 2 Core 1 Server 3 Core 2 Core 1 executor = ProcessPoolExecutor() executor.map(urlparse, urls) Python subprocess State Code Python subprocess State Code pickle.dumps() os.fork()

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joblib map functions over local machine cores by cleaning up stdlib facilities

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Python State Code Server 1 Core 2 Core 1 Server 2 Core 2 Core 1 Server 3 Core 2 Core 1 par = Parallel(n_jobs=2) do_urlparse = delayed(urlparse) par(do_urlparse(url) for url in urls) Python subprocess State Code Python subprocess State Code pickle.dumps() os.fork()

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ipyparallel map functions over a pet compute cluster

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Python State Code Server 1 Core 2 Core 1 Server 2 Core 2 Core 1 Server 3 Core 2 Core 1 rc = Client() rc[:].map_sync(urlparse, urls) Python State Code Python State Code ipengine Python State Code Python State Code Python State Code ipengine ipengine ipcontroller Python State Code pickle.dumps() pickle.dumps()

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pykafka map functions over a multi-consumer log

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Python State Code Server 1 Core 2 Core 1 Server 2 Core 2 Core 1 Server 3 Core 2 Core 1 consumer = ... # balanced while True: msg = consumer.consume() msg = json.loads(msg) urlparse(msg["url"]) Python State Code Python State Code Python State Code Python State Code Python State Code pykafka.producer Python State Code

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pystorm map functions over a stream of inputs to generate a stream of outputs

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Python State Code Server 1 Core 2 Core 1 Server 2 Core 2 Core 1 Server 3 Core 2 Core 1 Python State Code Python State Code Python State Code Python State Code pykafka.producer Python State Code multi-lang json protocol class UrlParser(Topology): url_spout = UrlSpout.spec(p=1) url_bolt = UrlBolt.spec(p=4, input=url_spout)

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pyspark map functions over a dataset representation to perform transformations and actions

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Python State Code Server 1 Core 2 Core 1 Server 2 Core 2 Core 1 Server 3 Core 2 Core 1 Python State Code Python State Code Python State Code Python State Code pyspark.SparkContext sc = SparkContext() file_rdd = sc.textFile(files) file_rdd.map(urlparse).take(1) cloudpickle py4j and binary pipes

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"lambda architecture"

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Parse.ly "Batch Layer" Topologies with Spark & S3 Parse.ly "Speed Layer" Topologies with Storm & Kafka Parse.ly Dashboards and APIs with Elasticsearch & Cassandra Parse.ly Raw Data Warehouse with Streaming & SQL Access Technology Component Summary

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parting thoughts

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the free lunch is over, but not how we thought

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multi-process, not multi-thread multi-node, not multi-core message passing, not shared memory ! heaps of data and streams of data

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GIL: it's a feature, not a bug. help us!
 pystorm pykafka streamparse

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Questions? tweet at @amontalenti