Python native implementation of the Spark RDD interface.
Project description
pysparkling
A native Python implementation of Spark’s RDD interface, but instead of being resilient and distributed it is just transient and local; but fast (lower latency than PySpark). It is a drop in replacement for PySpark’s SparkContext and RDD.
Use case: you have a pipeline that processes 100k input documents and converts them to normalized features. They are used to train a local scikit-learn classifier. The preprocessing is perfect for a full Spark task. Now, you want to use this trained classifier in an API endpoint. You need the same pre-processing pipeline for a single document per API call. This does not have to be done in parallel, but there should be only a small overhead in initialization and preferably no dependency on the JVM. This is what pysparkling is for.
Install
pip install pysparklingFeatures
Supports multiple URI schemes like s3n://, http:// and file://. Specify multiple files separated by comma. Resolves * and ? wildcards.
Handles .gz and .bz2 compressed files.
Parallelization via multiprocessing.Pool, concurrent.futures.ThreadPoolExecutor or any other Pool-like objects that have a map(func, iterable) method.
only dependencies: boto for AWS S3 and requests for http
Examples
The example source codes are included in tests/readme_example*.py.
Line counts: Count the lines in the *.py files in the tests directory and count only those lines that start with import:
import pysparkling
context = pysparkling.Context()
my_rdd = context.textFile('tests/*.py')
print('In tests/*.py: all lines={0}, with import={1}'.format(
my_rdd.count(),
my_rdd.filter(lambda l: l.startswith('import ')).count()
))which prints In tests/*.py: all lines=518, with import=11.
Common Crawl: More info on the dataset is in this blog post.
from pysparkling import Context
# read all the paths of warc and wat files of the latest Common Crawl
paths_rdd = Context().textFile(
's3n://aws-publicdatasets/common-crawl/crawl-data/CC-MAIN-2015-11/warc.paths.*,'
's3n://aws-publicdatasets/common-crawl/crawl-data/CC-MAIN-2015-11/wat.paths.gz'
)
print(paths_rdd.collect())which prints a long list of paths extracted from two gzip compressed files.
Human Microbiome Project: Get a random line without loading the entire dataset.
from pysparkling import Context
by_subject_rdd = Context().textFile(
's3n://human-microbiome-project/DEMO/HM16STR/46333/by_subject/*'
)
print(by_subject_rdd.takeSample(1))which prints out a line like [u'CAACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTTTCAGTATCGACGAAGC'].
API
Context
__init__(pool=None, serializer=None, deserializer=None, data_serializer=None, data_deserializer=None): takes a pool object (an object that has a map() method, e.g. a multiprocessing.Pool) to parallelize methods. To support functions and lambda functions, specify custom serializers and deserializers, e.g. serializer=dill.dumps, deserializer=dill.loads.
broadcast(var): returns an instance of Broadcast() and it’s values are accessed with value.
newRddId(): incrementing number
textFile(filename): load every line of a text file into a RDD. filename can contain a comma separated list of many files, ? and * wildcards, file paths on S3 (s3n://bucket_name/filename.txt) and local file paths (relative/path/my_text.txt, /absolut/path/my_text.txt or file:///absolute/file/path.txt). If the filename points to a folder containing part* files, those are resolved.
version: the version of pysparkling
RDD
aggregate(zeroValue, seqOp, combOp): aggregate value in partition with seqOp and combine with combOp
aggregateByKey(zeroValue, seqFunc, combFunc): aggregate by key
cache(): execute previous steps and cache result
cartesian(other): cartesian product
coalesce(): do nothing
collect(): return the underlying list
count(): get length of internal list
countApprox(): same as count()
countByKey: input is list of pairs, returns a dictionary
countByValue: input is a list, returns a dictionary
context(): return the context
distinct(): returns a new RDD containing the distinct elements
filter(func): return new RDD filtered with func
first(): return first element
flatMap(func): return a new RDD of a flattened map
flatMapValues(func): return new RDD
fold(zeroValue, op): aggregate elements
foldByKey(zeroValue, op): aggregate elements by key
foreach(func): apply func to every element
foreachPartition(func): apply func to every partition
getNumPartitions(): number of partitions
getPartitions(): returns an iterator over the partitions
groupBy(func): group by the output of func
groupByKey(): group by key where the RDD is of type [(key, value), …]
histogram(buckets): buckets can be a list or an int
id(): currently just returns None
intersection(other): return a new RDD with the intersection
isCheckpointed(): returns False
join(other): join
keyBy(func): creates tuple in new RDD
keys(): returns the keys of tuples in new RDD
leftOuterJoin(other): left outer join
lookup(key): return list of values for this key
map(func): apply func to every element and return a new RDD
mapPartitions(func): apply f to entire partitions
mapValues(func): apply func to value in (key, value) pairs and return a new RDD
max(): get the maximum element
mean(): mean
min(): get the minimum element
name(): RDD’s name
persist(): implemented as synonym for cache()
pipe(command): pipe the elements through an external command line tool
reduce(): reduce
reduceByKey(): reduce by key and return the new RDD
rightOuterJoin(other): right outer join
sample(withReplacement, fraction, seed=None): sample from the RDD
saveAsTextFile(path): save RDD as text file
subtract(other): return a new RDD without the elements in other
sum(): sum
take(n): get the first n elements
takeSample(n): get n random samples
Broadcast
value: access the value it stores
fileio
The functionality provided by this module is used in Context.textFile() for reading and in RDD.saveAsTextFile() for writing. Normally, you should not have to use this submodule directly.
Use environment variables AWS_SECRET_ACCESS_KEY and AWS_ACCESS_KEY_ID for auth and Use file paths of the form s3n://bucket_name/filename.txt.
Infers .gz and .bz2 compressions from the file name.
- File(file_name): file_name is either local, http, on S3 or …
[static] exists(path): check for existance of path
[static] resolve_filenames(expr): given a glob-like expression with * and ?, get a list of all matching filenames (either locally or on S3).
load(): return the contents as BytesIO
dump(stream): write the stream to the file
make_public(recursive=False): only for files on S3
Changelog
- v0.2.10 (2015-05-27)
fix fileio.codec import
support http://
- v0.2.8 (2015-05-26)
parallelized text file reading (and made it lazy)
parallelized take() and takeSample() that only computes required data partitions
add example: access Human Microbiome Project
- v0.2.6 (2015-05-21)
factor out fileio.fs and fileio.codec modules
merge WholeFile into File
improved handling of compressed files (backwards incompatible)
fileio interface changed to dump() and load() methods. Added make_public() for S3.
factor file related operations into fileio submodule
- v0.2.2 (2015-05-18)
compressions: .gz, .bz2
- v0.2.0 (2015-05-17)
proper handling of partitions
custom serializers, deserializers (for functions and data separately)
more tests for parallelization options
execution of distributed jobs is such that a chain of map() operations gets executed on workers without sending intermediate results back to the master
a few more methods for RDDs implemented
- v0.1.1 (2015-05-12)
implemented a few more RDD methods
changed handling of context in RDD
v0.1.0 (2015-05-09)
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