From 274b2384bb28f03eb034686e6e730e73af3ffaf3 Mon Sep 17 00:00:00 2001 From: Nicholas Chammas Date: Wed, 6 Aug 2014 14:29:55 -0400 Subject: [PATCH] [SPARK-2627] [PySpark] minor indentation changes --- python/pyspark/tests.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index da580f3ecb3bb..88a61176e51ab 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -594,7 +594,8 @@ def test_oldhadoop(self): "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/"} + "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( @@ -624,11 +625,13 @@ def test_newhadoop(self): valueConverter="org.apache.spark.api.python.WritableToDoubleArrayConverter").collect()) self.assertEqual(result, array_data) - conf = {"mapreduce.outputformat.class": + 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/"} + "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") @@ -1012,8 +1015,7 @@ class NumPyTests(PySparkTestCase): """General PySpark tests that depend on numpy """ def test_statcounter_array(self): - x = self.sc.parallelize( - [np.array([1.0, 1.0]), np.array([2.0, 2.0]), np.array([3.0, 3.0])]) + x = self.sc.parallelize([np.array([1.0, 1.0]), np.array([2.0, 2.0]), np.array([3.0, 3.0])]) s = x.stats() self.assertSequenceEqual([2.0, 2.0], s.mean().tolist()) self.assertSequenceEqual([1.0, 1.0], s.min().tolist())