This work is supported by Continuum Analytics and the XDATA Program as part of the Blaze Project

tl;dr We use dask to build a parallel Python list.

Introduction

This is the seventh in a sequence of posts constructing an out-of-core nd-array using NumPy, and dask. You can view these posts here:

1. Simple task scheduling,

2. Frontend usability

3. A multi-threaded scheduler

4. Matrix Multiply Benchmark

5. Spilling to disk

6. Slicing and Stacking

Today we take a break from ND-Arrays and show how task scheduling can attack other collections like the simple list of Python objects.

Unstructured Data

Often before we have an ndarray or a table/DataFrame we have unstructured data like log files. In this case tuned subsets of the language (e.g. numpy/pandas) aren’t sufficient, we need the full Python language.

My usual approach to the inconveniently large dump of log files is to use Python streaming iterators along with multiprocessing or IPython Parallel on a single large machine. I often write/speak about this workflow when discussing toolz.

This workflow grows complex for most users when you introduce many processes. To resolve this we build our normal tricks into a new dask.Bag collection.

Bag

In the same way that dask.array mimics NumPy operations (e.g. matrix multiply, slicing), dask.bag mimics functional operations like map, filter, reduce found in the standard library as well as many of the streaming functions found in toolz.

• Dask array = NumPy + threads

• Dask bag = Python/Toolz + processes

Example

Here’s the obligatory wordcount example

>>> from dask.bag import Bag

>>> b = Bag.from_filenames('data/*.txt')

>>> def stem(word):
...     """ Stem word to primitive form """
...     return word.lower().rstrip(",.!:;'-\"").lstrip("'\"")

>>> dict(b.map(str.split).map(concat).map(stem).frequencies())
{...}

We use all of our cores and stream through memory on each core. We use multiprocessing but could get fancier with some work.

Related Work

There are a lot of much larger much more powerful systems that have a similar API, notably Spark and DPark. If you have Big Data and need to use very many machines then you should stop reading this and go install them.

I mostly made dask.bag because

1. It was very easy given the work already done on dask.array

2. I often only need multiprocessing + a heavy machine

3. I wanted something trivially pip installable that didn’t use the JVM

But again, if you have Big Data, then this isn’t for you.

Design

As before, a Bag is just a dict holding tasks, along with a little meta data.

>>> d = {('x', 0): (range, 5),
...      ('x', 1): (range, 5),
...      ('x', 2): (range, 5)}

>>> from dask.bag import Bag
>>> b = Bag(d, 'x', npartitions=3)

In this way we break up one collection

[0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4]

into three independent pieces

[0, 1, 2, 3, 4]
[0, 1, 2, 3, 4]
[0, 1, 2, 3, 4]

When we abstractly operate on the large collection…

>>> b2 = b.map(lambda x: x * 10)

… we generate new tasks to operate on each of the components.

>>> b2.dask
{('x', 0): (range, 5),
 ('x', 1): (range, 5),
 ('x', 2): (range, 5)}
 ('bag-1', 0): (map, lambda x: x * 10, ('x', 0)),
 ('bag-1', 1): (map, lambda x: x * 10, ('x', 1)),
 ('bag-1', 2): (map, lambda x: x * 10, ('x', 2))}

And when we ask for concrete results (the call to list) we spin up a scheduler to execute the resulting dependency graph of tasks.

>>> list(b2)
[0, 10, 20, 30, 40, 0, 10, 20, 30, 40, 0, 10, 20, 30, 40]

More complex operations yield more complex dasks. Beware, dask code is pretty Lispy. Fortunately these dasks are internal; users don’t interact with them.

>>> iseven = lambda x: x % 2 == 0
>>> b3 = b.filter(iseven).count().dask
{'bag-3': (sum, [('bag-2', 1), ('bag-2', 2), ('bag-2', 0)]),
 ('bag-2', 0): (count,
                (filter, iseven, (range, 5))),
 ('bag-2', 1): (count,
                (filter, iseven, (range, 5))),
 ('bag-2', 2): (count,
                (filter, iseven, (range, 5)))}

The current interface for Bag has the following operations:

all             frequencies         min
any             join                product
count           map                 std
filter          map_partitions      sum
fold            max                 topk
foldby          mean                var

Manipulations of bags create task dependency graphs. We eventually execute these graphs in parallel.

Execution

We repurpose the threaded scheduler we used for arrays to support multiprocessing to provide parallelism even on pure Python code. We’re careful to avoid unnecessary data transfer. None of the operations listed above requires significant communication. Notably we don’t have any concept of shuffling or scatter/gather.

We use dill to take care to serialize functions properly and collect/report errors, two issues that plague naive use of multiprocessing in Python.

>>> list(b.map(lambda x: x * 10))  # This works!
[0, 10, 20, 30, 40, 0, 10, 20, 30, 40, 0, 10, 20, 30, 40]

>>> list(b.map(lambda x: x / 0))   # This errs gracefully!
ZeroDivisionError:  Execption in remote Process

integer division or modulo by zero

Traceback:
    ...

These tricks remove need for user expertise.

Productive Sweet Spot

I think that there is a productive sweet spot in the following configuration

1. Pure Python functions

2. Streaming/lazy data

3. Multiprocessing

4. A single large machine or a few machines in an informal cluster

This setup is common and it’s capable of handling terabyte scale workflows. In my brief experience people rarely take this route. They use single-threaded in-memory Python until it breaks, and then seek out Big Data Infrastructure like Hadoop/Spark at relatively high productivity overhead.

Your workstation can scale bigger than you think.

Example

Here is about a gigabyte of network flow data, recording which computers made connections to which other computers on the UC-Berkeley campus in 1996.

846890339:661920 846890339:755475 846890340:197141 168.237.7.10:1163 83.153.38.208:80 2 8 4294967295 4294967295 846615753 176 2462 39 GET 21068906053917068819..html HTTP/1.0

846890340:989181 846890341:2147 846890341:2268 13.35.251.117:1269 207.83.232.163:80 10 0 842099997 4294967295 4294967295 64 1 38 GET 20271810743860818265..gif HTTP/1.0

846890341:80714 846890341:90331 846890341:90451 13.35.251.117:1270 207.83.232.163:80 10 0 842099995 4294967295 4294967295 64 1 38 GET 38127854093537985420..gif HTTP/1.0

This is actually relatively clean. Many of the fields are space delimited (not all) and I’ve already compiled and run the decade old C-code needed to decompress it from its original format.

Lets use Bag and regular expressions to parse this.

In [1]: from dask.bag import Bag, into

In [2]: b = Bag.from_filenames('UCB-home-IP*.log')

In [3]: import re

In [4]: pattern = """
   ...: (?P<request_time>\d+:\d+)
   ...: (?P<response_start>\d+:\d+)
   ...: (?P<response_end>\d+:\d+)
   ...: (?P<client_ip>\d+\.\d+\.\d+\.\d+):(?P<client_port>\d+)
   ...: (?P<server_ip>\d+\.\d+\.\d+\.\d+):(?P<server_port>\d+)
   ...: (?P<client_headers>\d+)
   ...: (?P<server_headers>\d+)
   ...: (?P<if_modified_since>\d+)
   ...: (?P<response_header_length>\d+)
   ...: (?P<response_data_length>\d+)
   ...: (?P<request_url_length>\d+)
   ...: (?P<expires>\d+)
   ...: (?P<last_modified>\d+)
   ...: (?P<method>\w+)
   ...: (?P<domain>\d+..)\.(?P<extension>\w*)(?P<rest_of_url>\S*)
   ...: (?P<protocol>.*)""".strip().replace('\n', '\s+')

In [5]: prog = re.compile(pattern)

In [6]: records = b.map(prog.match).map(lambda m: m.groupdict())

This returns instantly. We only compute results when necessary. We trigger computation by calling list.

In [7]: list(records.take(1))
Out[7]:
[{'client_headers': '2',
  'client_ip': '168.237.7.10',
  'client_port': '1163',
  'domain': '21068906053917068819.',
  'expires': '2462',
  'extension': 'html',
  'if_modified_since': '4294967295',
  'last_modified': '39',
  'method': 'GET',
  'protocol': 'HTTP/1.0',
  'request_time': '846890339:661920',
  'request_url_length': '176',
  'response_data_length': '846615753',
  'response_end': '846890340:197141',
  'response_header_length': '4294967295',
  'response_start': '846890339:755475',
  'rest_of_url': '',
  'server_headers': '8',
  'server_ip': '83.153.38.208',
  'server_port': '80'}]

Because bag operates lazily this small result also returns immediately.

To demonstrate depth we find the ten client/server pairs with the most connections.

In [8]: counts = records.pluck(['client_ip', 'server_ip']).frequencies()

In [9]: %time list(counts.topk(10, key=lambda x: x[1]))
CPU times: user 11.2 s, sys: 1.15 s, total: 12.3 s
Wall time: 50.4 s
Out[9]:
[(('247.193.34.56', '243.182.247.102'), 35353),
 (('172.219.28.251', '47.61.128.1'), 22333),
 (('240.97.200.0', '108.146.202.184'), 17492),
 (('229.112.177.58', '47.61.128.1'), 12993),
 (('146.214.34.69', '119.153.78.6'), 12554),
 (('17.32.139.174', '179.135.20.36'), 10166),
 (('97.166.76.88', '65.81.49.125'), 8155),
 (('55.156.159.21', '157.229.248.255'), 7533),
 (('55.156.159.21', '124.77.75.86'), 7506),
 (('55.156.159.21', '97.5.181.76'), 7501)]

Comparison with Spark

First, it is silly and unfair to compare with PySpark running locally. PySpark offers much more in a distributed context.

In [1]: import pyspark

In [2]: sc = pyspark.SparkContext('local')

In [3]: from glob import glob
In [4]: filenames = sorted(glob('UCB-home-*.log'))
In [5]: rdd = sc.parallelize(filenames, numSlices=4)

In [6]: import re
In [7]: pattern = ...
In [8]: prog = re.compile(pattern)

In [9]: lines = rdd.flatMap(lambda fn: list(open(fn)))
In [10]: records = lines.map(lambda line: prog.match(line).groupdict())
In [11]: ips = records.map(lambda rec: (rec['client_ip'], rec['server_ip']))

In [12]: from toolz import topk
In [13]: %time dict(topk(10, ips.countByValue().items(), key=1))
CPU times: user 1.32 s, sys: 52.2 ms, total: 1.37 s
Wall time: 1min 21s
Out[13]:
{('146.214.34.69', '119.153.78.6'): 12554,
 ('17.32.139.174', '179.135.20.36'): 10166,
 ('172.219.28.251', '47.61.128.1'): 22333,
 ('229.112.177.58', '47.61.128.1'): 12993,
 ('240.97.200.0', '108.146.202.184'): 17492,
 ('247.193.34.56', '243.182.247.102'): 35353,
 ('55.156.159.21', '124.77.75.86'): 7506,
 ('55.156.159.21', '157.229.248.255'): 7533,
 ('55.156.159.21', '97.5.181.76'): 7501,
 ('97.166.76.88', '65.81.49.125'): 8155}

So, given a compute-bound mostly embarrassingly parallel task (regexes are comparatively expensive) on a single machine they are comparable.

Reasons you would want to use Spark

• You want to use many machines and interact with HDFS

• Shuffling operations

Reasons you would want to use dask.bag

• Trivial installation

• No mucking about with JVM heap sizes or config files

• Nice error reporting. Spark error reporting includes the typical giant Java Stack trace that comes with any JVM solution.

• Easier/simpler for Python programmers to hack on. The implementation is 350 lines including comments.

Again, this is really just a toy experiment to show that the dask model isn’t just about arrays. I absolutely do not want to throw Dask in the ring with Spark.

Conclusion

However I do want to stress the importance of single-machine parallelism. Dask.bag targets this application well and leverages common hardware in a simple way that is both natural and accessible to most Python programmers.

A skilled developer could extend this to work in a distributed memory context. The logic to create the task dependency graphs is separate from the scheduler.

Special thanks to Erik Welch for finely crafting the dask optimization passes that keep the data flowly smoothly.

Towards Out-of-core ND-Arrays -- Slicing and Stacking   Towards Out-of-core DataFrames

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