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author | Kan Zhang <kzhang@apache.org> | 2014-07-30 13:19:05 -0700 |
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committer | Josh Rosen <joshrosen@apache.org> | 2014-07-30 13:19:05 -0700 |
commit | 94d1f46fc43c0cb85125f757fb40db9271caf1f4 (patch) | |
tree | 8878443a963ad6ce5ba3af679567d893c8df70cc /python/pyspark/tests.py | |
parent | 437dc8c5b54f0dcf9564c1fb07e8dce9e771c8cd (diff) | |
download | spark-94d1f46fc43c0cb85125f757fb40db9271caf1f4.tar.gz spark-94d1f46fc43c0cb85125f757fb40db9271caf1f4.tar.bz2 spark-94d1f46fc43c0cb85125f757fb40db9271caf1f4.zip |
[SPARK-2024] Add saveAsSequenceFile to PySpark
JIRA issue: https://issues.apache.org/jira/browse/SPARK-2024
This PR is a followup to #455 and adds capabilities for saving PySpark RDDs using SequenceFile or any Hadoop OutputFormats.
* Added RDD methods ```saveAsSequenceFile```, ```saveAsHadoopFile``` and ```saveAsHadoopDataset```, for both old and new MapReduce APIs.
* Default converter for converting common data types to Writables. Users may specify custom converters to convert to desired data types.
* No out-of-box support for reading/writing arrays, since ArrayWritable itself doesn't have a no-arg constructor for creating an empty instance upon reading. Users need to provide ArrayWritable subtypes. Custom converters for converting arrays to suitable ArrayWritable subtypes are also needed when writing. When reading, the default converter will convert any custom ArrayWritable subtypes to ```Object[]``` and they get pickled to Python tuples.
* Added HBase and Cassandra output examples to show how custom output formats and converters can be used.
cc MLnick mateiz ahirreddy pwendell
Author: Kan Zhang <kzhang@apache.org>
Closes #1338 from kanzhang/SPARK-2024 and squashes the following commits:
c01e3ef [Kan Zhang] [SPARK-2024] code formatting
6591e37 [Kan Zhang] [SPARK-2024] renaming pickled -> pickledRDD
d998ad6 [Kan Zhang] [SPARK-2024] refectoring to get method params below 10
57a7a5e [Kan Zhang] [SPARK-2024] correcting typo
75ca5bd [Kan Zhang] [SPARK-2024] Better type checking for batch serialized RDD
0bdec55 [Kan Zhang] [SPARK-2024] Refactoring newly added tests
9f39ff4 [Kan Zhang] [SPARK-2024] Adding 2 saveAsHadoopDataset tests
0c134f3 [Kan Zhang] [SPARK-2024] Test refactoring and adding couple unbatched cases
7a176df [Kan Zhang] [SPARK-2024] Add saveAsSequenceFile to PySpark
Diffstat (limited to 'python/pyspark/tests.py')
-rw-r--r-- | python/pyspark/tests.py | 317 |
1 files changed, 304 insertions, 13 deletions
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index 8486c8595b..c29deb9574 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -19,6 +19,7 @@ Unit tests for PySpark; additional tests are implemented as doctests in individual modules. """ +from array import array from fileinput import input from glob import glob import os @@ -327,6 +328,17 @@ class TestInputFormat(PySparkTestCase): ed = [(1.0, u'aa'), (1.0, u'aa'), (2.0, u'aa'), (2.0, u'bb'), (2.0, u'bb'), (3.0, u'cc')] self.assertEqual(doubles, ed) + bytes = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfbytes/", + "org.apache.hadoop.io.IntWritable", + "org.apache.hadoop.io.BytesWritable").collect()) + ebs = [(1, bytearray('aa', 'utf-8')), + (1, bytearray('aa', 'utf-8')), + (2, bytearray('aa', 'utf-8')), + (2, bytearray('bb', 'utf-8')), + (2, bytearray('bb', 'utf-8')), + (3, bytearray('cc', 'utf-8'))] + self.assertEqual(bytes, ebs) + text = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sftext/", "org.apache.hadoop.io.Text", "org.apache.hadoop.io.Text").collect()) @@ -353,14 +365,34 @@ class TestInputFormat(PySparkTestCase): maps = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfmap/", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.MapWritable").collect()) - em = [(1, {2.0: u'aa'}), + em = [(1, {}), (1, {3.0: u'bb'}), (2, {1.0: u'aa'}), (2, {1.0: u'cc'}), - (2, {3.0: u'bb'}), (3, {2.0: u'dd'})] self.assertEqual(maps, em) + # arrays get pickled to tuples by default + tuples = sorted(self.sc.sequenceFile( + basepath + "/sftestdata/sfarray/", + "org.apache.hadoop.io.IntWritable", + "org.apache.spark.api.python.DoubleArrayWritable").collect()) + et = [(1, ()), + (2, (3.0, 4.0, 5.0)), + (3, (4.0, 5.0, 6.0))] + self.assertEqual(tuples, et) + + # with custom converters, primitive arrays can stay as arrays + arrays = sorted(self.sc.sequenceFile( + basepath + "/sftestdata/sfarray/", + "org.apache.hadoop.io.IntWritable", + "org.apache.spark.api.python.DoubleArrayWritable", + valueConverter="org.apache.spark.api.python.WritableToDoubleArrayConverter").collect()) + ea = [(1, array('d')), + (2, array('d', [3.0, 4.0, 5.0])), + (3, array('d', [4.0, 5.0, 6.0]))] + self.assertEqual(arrays, ea) + clazz = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfclass/", "org.apache.hadoop.io.Text", "org.apache.spark.api.python.TestWritable").collect()) @@ -369,6 +401,12 @@ class TestInputFormat(PySparkTestCase): u'double': 54.0, u'int': 123, u'str': u'test1'}) self.assertEqual(clazz[0], ec) + unbatched_clazz = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfclass/", + "org.apache.hadoop.io.Text", + "org.apache.spark.api.python.TestWritable", + batchSize=1).collect()) + self.assertEqual(unbatched_clazz[0], ec) + def test_oldhadoop(self): basepath = self.tempdir.name ints = sorted(self.sc.hadoopFile(basepath + "/sftestdata/sfint/", @@ -379,10 +417,11 @@ class TestInputFormat(PySparkTestCase): self.assertEqual(ints, ei) hellopath = os.path.join(SPARK_HOME, "python/test_support/hello.txt") - hello = self.sc.hadoopFile(hellopath, - "org.apache.hadoop.mapred.TextInputFormat", - "org.apache.hadoop.io.LongWritable", - "org.apache.hadoop.io.Text").collect() + oldconf = {"mapred.input.dir" : hellopath} + hello = self.sc.hadoopRDD("org.apache.hadoop.mapred.TextInputFormat", + "org.apache.hadoop.io.LongWritable", + "org.apache.hadoop.io.Text", + conf=oldconf).collect() result = [(0, u'Hello World!')] self.assertEqual(hello, result) @@ -397,10 +436,11 @@ class TestInputFormat(PySparkTestCase): self.assertEqual(ints, ei) hellopath = os.path.join(SPARK_HOME, "python/test_support/hello.txt") - hello = self.sc.newAPIHadoopFile(hellopath, - "org.apache.hadoop.mapreduce.lib.input.TextInputFormat", - "org.apache.hadoop.io.LongWritable", - "org.apache.hadoop.io.Text").collect() + newconf = {"mapred.input.dir" : hellopath} + hello = self.sc.newAPIHadoopRDD("org.apache.hadoop.mapreduce.lib.input.TextInputFormat", + "org.apache.hadoop.io.LongWritable", + "org.apache.hadoop.io.Text", + conf=newconf).collect() result = [(0, u'Hello World!')] self.assertEqual(hello, result) @@ -435,16 +475,267 @@ class TestInputFormat(PySparkTestCase): "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.Text")) - def test_converter(self): + def test_converters(self): + # use of custom converters basepath = self.tempdir.name maps = sorted(self.sc.sequenceFile( basepath + "/sftestdata/sfmap/", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.MapWritable", - valueConverter="org.apache.spark.api.python.TestConverter").collect()) - em = [(1, [2.0]), (1, [3.0]), (2, [1.0]), (2, [1.0]), (2, [3.0]), (3, [2.0])] + keyConverter="org.apache.spark.api.python.TestInputKeyConverter", + valueConverter="org.apache.spark.api.python.TestInputValueConverter").collect()) + em = [(u'\x01', []), + (u'\x01', [3.0]), + (u'\x02', [1.0]), + (u'\x02', [1.0]), + (u'\x03', [2.0])] + self.assertEqual(maps, em) + +class TestOutputFormat(PySparkTestCase): + + def setUp(self): + PySparkTestCase.setUp(self) + self.tempdir = tempfile.NamedTemporaryFile(delete=False) + os.unlink(self.tempdir.name) + + def tearDown(self): + PySparkTestCase.tearDown(self) + shutil.rmtree(self.tempdir.name, ignore_errors=True) + + def test_sequencefiles(self): + basepath = self.tempdir.name + ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')] + self.sc.parallelize(ei).saveAsSequenceFile(basepath + "/sfint/") + ints = sorted(self.sc.sequenceFile(basepath + "/sfint/").collect()) + self.assertEqual(ints, ei) + + ed = [(1.0, u'aa'), (1.0, u'aa'), (2.0, u'aa'), (2.0, u'bb'), (2.0, u'bb'), (3.0, u'cc')] + self.sc.parallelize(ed).saveAsSequenceFile(basepath + "/sfdouble/") + doubles = sorted(self.sc.sequenceFile(basepath + "/sfdouble/").collect()) + self.assertEqual(doubles, ed) + + ebs = [(1, bytearray(b'\x00\x07spam\x08')), (2, bytearray(b'\x00\x07spam\x08'))] + self.sc.parallelize(ebs).saveAsSequenceFile(basepath + "/sfbytes/") + bytes = sorted(self.sc.sequenceFile(basepath + "/sfbytes/").collect()) + self.assertEqual(bytes, ebs) + + et = [(u'1', u'aa'), + (u'2', u'bb'), + (u'3', u'cc')] + self.sc.parallelize(et).saveAsSequenceFile(basepath + "/sftext/") + text = sorted(self.sc.sequenceFile(basepath + "/sftext/").collect()) + self.assertEqual(text, et) + + eb = [(1, False), (1, True), (2, False), (2, False), (2, True), (3, True)] + self.sc.parallelize(eb).saveAsSequenceFile(basepath + "/sfbool/") + bools = sorted(self.sc.sequenceFile(basepath + "/sfbool/").collect()) + self.assertEqual(bools, eb) + + en = [(1, None), (1, None), (2, None), (2, None), (2, None), (3, None)] + self.sc.parallelize(en).saveAsSequenceFile(basepath + "/sfnull/") + nulls = sorted(self.sc.sequenceFile(basepath + "/sfnull/").collect()) + self.assertEqual(nulls, en) + + em = [(1, {}), + (1, {3.0: u'bb'}), + (2, {1.0: u'aa'}), + (2, {1.0: u'cc'}), + (3, {2.0: u'dd'})] + self.sc.parallelize(em).saveAsSequenceFile(basepath + "/sfmap/") + maps = sorted(self.sc.sequenceFile(basepath + "/sfmap/").collect()) self.assertEqual(maps, em) + def test_oldhadoop(self): + basepath = self.tempdir.name + dict_data = [(1, {}), + (1, {"row1" : 1.0}), + (2, {"row2" : 2.0})] + self.sc.parallelize(dict_data).saveAsHadoopFile( + basepath + "/oldhadoop/", + "org.apache.hadoop.mapred.SequenceFileOutputFormat", + "org.apache.hadoop.io.IntWritable", + "org.apache.hadoop.io.MapWritable") + result = sorted(self.sc.hadoopFile( + basepath + "/oldhadoop/", + "org.apache.hadoop.mapred.SequenceFileInputFormat", + "org.apache.hadoop.io.IntWritable", + "org.apache.hadoop.io.MapWritable").collect()) + self.assertEqual(result, dict_data) + + conf = { + "mapred.output.format.class" : "org.apache.hadoop.mapred.SequenceFileOutputFormat", + "mapred.output.key.class" : "org.apache.hadoop.io.IntWritable", + "mapred.output.value.class" : "org.apache.hadoop.io.MapWritable", + "mapred.output.dir" : basepath + "/olddataset/"} + self.sc.parallelize(dict_data).saveAsHadoopDataset(conf) + input_conf = {"mapred.input.dir" : basepath + "/olddataset/"} + old_dataset = sorted(self.sc.hadoopRDD( + "org.apache.hadoop.mapred.SequenceFileInputFormat", + "org.apache.hadoop.io.IntWritable", + "org.apache.hadoop.io.MapWritable", + conf=input_conf).collect()) + self.assertEqual(old_dataset, dict_data) + + def test_newhadoop(self): + basepath = self.tempdir.name + # use custom ArrayWritable types and converters to handle arrays + array_data = [(1, array('d')), + (1, array('d', [1.0, 2.0, 3.0])), + (2, array('d', [3.0, 4.0, 5.0]))] + self.sc.parallelize(array_data).saveAsNewAPIHadoopFile( + basepath + "/newhadoop/", + "org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat", + "org.apache.hadoop.io.IntWritable", + "org.apache.spark.api.python.DoubleArrayWritable", + valueConverter="org.apache.spark.api.python.DoubleArrayToWritableConverter") + result = sorted(self.sc.newAPIHadoopFile( + basepath + "/newhadoop/", + "org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat", + "org.apache.hadoop.io.IntWritable", + "org.apache.spark.api.python.DoubleArrayWritable", + valueConverter="org.apache.spark.api.python.WritableToDoubleArrayConverter").collect()) + self.assertEqual(result, array_data) + + conf = {"mapreduce.outputformat.class" : + "org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat", + "mapred.output.key.class" : "org.apache.hadoop.io.IntWritable", + "mapred.output.value.class" : "org.apache.spark.api.python.DoubleArrayWritable", + "mapred.output.dir" : basepath + "/newdataset/"} + self.sc.parallelize(array_data).saveAsNewAPIHadoopDataset(conf, + valueConverter="org.apache.spark.api.python.DoubleArrayToWritableConverter") + input_conf = {"mapred.input.dir" : basepath + "/newdataset/"} + new_dataset = sorted(self.sc.newAPIHadoopRDD( + "org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat", + "org.apache.hadoop.io.IntWritable", + "org.apache.spark.api.python.DoubleArrayWritable", + valueConverter="org.apache.spark.api.python.WritableToDoubleArrayConverter", + conf=input_conf).collect()) + self.assertEqual(new_dataset, array_data) + + def test_newolderror(self): + basepath = self.tempdir.name + rdd = self.sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x )) + self.assertRaises(Exception, lambda: rdd.saveAsHadoopFile( + basepath + "/newolderror/saveAsHadoopFile/", + "org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat")) + self.assertRaises(Exception, lambda: rdd.saveAsNewAPIHadoopFile( + basepath + "/newolderror/saveAsNewAPIHadoopFile/", + "org.apache.hadoop.mapred.SequenceFileOutputFormat")) + + def test_bad_inputs(self): + basepath = self.tempdir.name + rdd = self.sc.parallelize(range(1, 4)).map(lambda x: (x, "a" * x )) + self.assertRaises(Exception, lambda: rdd.saveAsHadoopFile( + basepath + "/badinputs/saveAsHadoopFile/", + "org.apache.hadoop.mapred.NotValidOutputFormat")) + self.assertRaises(Exception, lambda: rdd.saveAsNewAPIHadoopFile( + basepath + "/badinputs/saveAsNewAPIHadoopFile/", + "org.apache.hadoop.mapreduce.lib.output.NotValidOutputFormat")) + + def test_converters(self): + # use of custom converters + basepath = self.tempdir.name + data = [(1, {3.0: u'bb'}), + (2, {1.0: u'aa'}), + (3, {2.0: u'dd'})] + self.sc.parallelize(data).saveAsNewAPIHadoopFile( + basepath + "/converters/", + "org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat", + keyConverter="org.apache.spark.api.python.TestOutputKeyConverter", + valueConverter="org.apache.spark.api.python.TestOutputValueConverter") + converted = sorted(self.sc.sequenceFile(basepath + "/converters/").collect()) + expected = [(u'1', 3.0), + (u'2', 1.0), + (u'3', 2.0)] + self.assertEqual(converted, expected) + + def test_reserialization(self): + basepath = self.tempdir.name + x = range(1, 5) + y = range(1001, 1005) + data = zip(x, y) + rdd = self.sc.parallelize(x).zip(self.sc.parallelize(y)) + rdd.saveAsSequenceFile(basepath + "/reserialize/sequence") + result1 = sorted(self.sc.sequenceFile(basepath + "/reserialize/sequence").collect()) + self.assertEqual(result1, data) + + rdd.saveAsHadoopFile(basepath + "/reserialize/hadoop", + "org.apache.hadoop.mapred.SequenceFileOutputFormat") + result2 = sorted(self.sc.sequenceFile(basepath + "/reserialize/hadoop").collect()) + self.assertEqual(result2, data) + + rdd.saveAsNewAPIHadoopFile(basepath + "/reserialize/newhadoop", + "org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat") + result3 = sorted(self.sc.sequenceFile(basepath + "/reserialize/newhadoop").collect()) + self.assertEqual(result3, data) + + conf4 = { + "mapred.output.format.class" : "org.apache.hadoop.mapred.SequenceFileOutputFormat", + "mapred.output.key.class" : "org.apache.hadoop.io.IntWritable", + "mapred.output.value.class" : "org.apache.hadoop.io.IntWritable", + "mapred.output.dir" : basepath + "/reserialize/dataset"} + rdd.saveAsHadoopDataset(conf4) + result4 = sorted(self.sc.sequenceFile(basepath + "/reserialize/dataset").collect()) + self.assertEqual(result4, data) + + conf5 = {"mapreduce.outputformat.class" : + "org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat", + "mapred.output.key.class" : "org.apache.hadoop.io.IntWritable", + "mapred.output.value.class" : "org.apache.hadoop.io.IntWritable", + "mapred.output.dir" : basepath + "/reserialize/newdataset"} + rdd.saveAsNewAPIHadoopDataset(conf5) + result5 = sorted(self.sc.sequenceFile(basepath + "/reserialize/newdataset").collect()) + self.assertEqual(result5, data) + + def test_unbatched_save_and_read(self): + basepath = self.tempdir.name + ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')] + self.sc.parallelize(ei, numSlices=len(ei)).saveAsSequenceFile( + basepath + "/unbatched/") + + unbatched_sequence = sorted(self.sc.sequenceFile(basepath + "/unbatched/", + batchSize=1).collect()) + self.assertEqual(unbatched_sequence, ei) + + unbatched_hadoopFile = sorted(self.sc.hadoopFile(basepath + "/unbatched/", + "org.apache.hadoop.mapred.SequenceFileInputFormat", + "org.apache.hadoop.io.IntWritable", + "org.apache.hadoop.io.Text", + batchSize=1).collect()) + self.assertEqual(unbatched_hadoopFile, ei) + + unbatched_newAPIHadoopFile = sorted(self.sc.newAPIHadoopFile(basepath + "/unbatched/", + "org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat", + "org.apache.hadoop.io.IntWritable", + "org.apache.hadoop.io.Text", + batchSize=1).collect()) + self.assertEqual(unbatched_newAPIHadoopFile, ei) + + oldconf = {"mapred.input.dir" : basepath + "/unbatched/"} + unbatched_hadoopRDD = sorted(self.sc.hadoopRDD( + "org.apache.hadoop.mapred.SequenceFileInputFormat", + "org.apache.hadoop.io.IntWritable", + "org.apache.hadoop.io.Text", + conf=oldconf, + batchSize=1).collect()) + self.assertEqual(unbatched_hadoopRDD, ei) + + newconf = {"mapred.input.dir" : basepath + "/unbatched/"} + unbatched_newAPIHadoopRDD = sorted(self.sc.newAPIHadoopRDD( + "org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat", + "org.apache.hadoop.io.IntWritable", + "org.apache.hadoop.io.Text", + conf=newconf, + batchSize=1).collect()) + self.assertEqual(unbatched_newAPIHadoopRDD, ei) + + def test_malformed_RDD(self): + basepath = self.tempdir.name + # non-batch-serialized RDD[[(K, V)]] should be rejected + data = [[(1, "a")], [(2, "aa")], [(3, "aaa")]] + rdd = self.sc.parallelize(data, numSlices=len(data)) + self.assertRaises(Exception, lambda: rdd.saveAsSequenceFile( + basepath + "/malformed/sequence")) class TestDaemon(unittest.TestCase): def connect(self, port): |