diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index eda0b60f8b1e7..a70c664a71fdb 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -86,7 +86,7 @@ class LogisticRegressionModel(LinearClassificationModel):
     ...     LabeledPoint(0.0, [0.0, 1.0]),
     ...     LabeledPoint(1.0, [1.0, 0.0]),
     ... ]
-    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data))
+    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data), iterations=10)
     >>> lrm.predict([1.0, 0.0])
     1
     >>> lrm.predict([0.0, 1.0])
@@ -95,7 +95,7 @@ class LogisticRegressionModel(LinearClassificationModel):
     [1, 0]
     >>> lrm.clearThreshold()
     >>> lrm.predict([0.0, 1.0])
-    0.123...
+    0.279...
 
     >>> sparse_data = [
     ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
@@ -103,7 +103,7 @@ class LogisticRegressionModel(LinearClassificationModel):
     ...     LabeledPoint(0.0, SparseVector(2, {0: 1.0})),
     ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
     ... ]
-    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data))
+    >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data), iterations=10)
     >>> lrm.predict(array([0.0, 1.0]))
     1
     >>> lrm.predict(array([1.0, 0.0]))
@@ -129,7 +129,8 @@ class LogisticRegressionModel(LinearClassificationModel):
     ...     LabeledPoint(1.0, [1.0, 0.0, 0.0]),
     ...     LabeledPoint(2.0, [0.0, 0.0, 1.0])
     ... ]
-    >>> mcm = LogisticRegressionWithLBFGS.train(data=sc.parallelize(multi_class_data), numClasses=3)
+    >>> data = sc.parallelize(multi_class_data)
+    >>> mcm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3)
     >>> mcm.predict([0.0, 0.5, 0.0])
     0
     >>> mcm.predict([0.8, 0.0, 0.0])
@@ -298,7 +299,7 @@ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType
         ...     LabeledPoint(0.0, [0.0, 1.0]),
         ...     LabeledPoint(1.0, [1.0, 0.0]),
         ... ]
-        >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data))
+        >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10)
         >>> lrm.predict([1.0, 0.0])
         1
         >>> lrm.predict([0.0, 1.0])
@@ -330,14 +331,14 @@ class SVMModel(LinearClassificationModel):
     ...     LabeledPoint(1.0, [2.0]),
     ...     LabeledPoint(1.0, [3.0])
     ... ]
-    >>> svm = SVMWithSGD.train(sc.parallelize(data))
+    >>> svm = SVMWithSGD.train(sc.parallelize(data), iterations=10)
     >>> svm.predict([1.0])
     1
     >>> svm.predict(sc.parallelize([[1.0]])).collect()
     [1]
     >>> svm.clearThreshold()
     >>> svm.predict(array([1.0]))
-    1.25...
+    1.44...
 
     >>> sparse_data = [
     ...     LabeledPoint(0.0, SparseVector(2, {0: -1.0})),
@@ -345,7 +346,7 @@ class SVMModel(LinearClassificationModel):
     ...     LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
     ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
     ... ]
-    >>> svm = SVMWithSGD.train(sc.parallelize(sparse_data))
+    >>> svm = SVMWithSGD.train(sc.parallelize(sparse_data), iterations=10)
     >>> svm.predict(SparseVector(2, {1: 1.0}))
     1
     >>> svm.predict(SparseVector(2, {0: -1.0}))
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index a0117c57133ae..4bc6351bdf02f 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -108,7 +108,8 @@ class LinearRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, [2.0]),
     ...     LabeledPoint(2.0, [3.0])
     ... ]
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=np.array([1.0]))
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10,
+    ...     initialWeights=np.array([1.0]))
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
@@ -135,12 +136,13 @@ class LinearRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
     ...     LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
     ... ]
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10,
+    ...     initialWeights=array([1.0]))
     >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0,
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
     ...    miniBatchFraction=1.0, initialWeights=array([1.0]), regParam=0.1, regType="l2",
     ...    intercept=True, validateData=True)
     >>> abs(lrm.predict(array([0.0])) - 0) < 0.5
@@ -238,7 +240,7 @@ class LassoModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, [2.0]),
     ...     LabeledPoint(2.0, [3.0])
     ... ]
-    >>> lrm = LassoWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
+    >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=10, initialWeights=array([1.0]))
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
@@ -265,12 +267,13 @@ class LassoModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
     ...     LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
     ... ]
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10,
+    ...     initialWeights=array([1.0]))
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
-    >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=100, step=1.0,
+    >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
     ...     regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True,
     ...     validateData=True)
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
@@ -321,7 +324,8 @@ class RidgeRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, [2.0]),
     ...     LabeledPoint(2.0, [3.0])
     ... ]
-    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
+    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=10,
+    ...     initialWeights=array([1.0]))
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
@@ -348,12 +352,13 @@ class RidgeRegressionModel(LinearRegressionModelBase):
     ...     LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
     ...     LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
     ... ]
-    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
+    >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10,
+    ...     initialWeights=array([1.0]))
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
     True
     >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
     True
-    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0,
+    >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
     ...     regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True,
     ...     validateData=True)
     >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
@@ -396,7 +401,7 @@ def _test():
     from pyspark import SparkContext
     import pyspark.mllib.regression
     globs = pyspark.mllib.regression.__dict__.copy()
-    globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
+    globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
     (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 849c88341a967..1487470f56d43 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -36,6 +36,7 @@
 else:
     import unittest
 
+from pyspark import SparkContext
 from pyspark.mllib.common import _to_java_object_rdd
 from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector,\
     DenseMatrix, SparseMatrix, Vectors, Matrices
@@ -47,7 +48,6 @@
 from pyspark.mllib.feature import StandardScaler
 from pyspark.serializers import PickleSerializer
 from pyspark.sql import SQLContext
-from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
 
 _have_scipy = False
 try:
@@ -58,6 +58,12 @@
     pass
 
 ser = PickleSerializer()
+sc = SparkContext('local[4]', "MLlib tests")
+
+
+class MLlibTestCase(unittest.TestCase):
+    def setUp(self):
+        self.sc = sc
 
 
 def _squared_distance(a, b):
@@ -67,7 +73,7 @@ def _squared_distance(a, b):
         return b.squared_distance(a)
 
 
-class VectorTests(PySparkTestCase):
+class VectorTests(MLlibTestCase):
 
     def _test_serialize(self, v):
         self.assertEqual(v, ser.loads(ser.dumps(v)))
@@ -196,7 +202,7 @@ def test_sparse_matrix(self):
         self.assertTrue(array_equal(sm1t.toArray(), expected))
 
 
-class ListTests(PySparkTestCase):
+class ListTests(MLlibTestCase):
 
     """
     Test MLlib algorithms on plain lists, to make sure they're passed through
@@ -239,7 +245,7 @@ def test_gmm(self):
             [-6, -7],
         ])
         clusters = GaussianMixture.train(data, 2, convergenceTol=0.001,
-                                         maxIterations=100, seed=56)
+                                         maxIterations=10, seed=56)
         labels = clusters.predict(data).collect()
         self.assertEquals(labels[0], labels[1])
         self.assertEquals(labels[2], labels[3])
@@ -250,9 +256,9 @@ def test_gmm_deterministic(self):
         y = range(0, 100, 10)
         data = self.sc.parallelize([[a, b] for a, b in zip(x, y)])
         clusters1 = GaussianMixture.train(data, 5, convergenceTol=0.001,
-                                          maxIterations=100, seed=63)
+                                          maxIterations=10, seed=63)
         clusters2 = GaussianMixture.train(data, 5, convergenceTol=0.001,
-                                          maxIterations=100, seed=63)
+                                          maxIterations=10, seed=63)
         for c1, c2 in zip(clusters1.weights, clusters2.weights):
             self.assertEquals(round(c1, 7), round(c2, 7))
 
@@ -271,13 +277,13 @@ def test_classification(self):
 
         temp_dir = tempfile.mkdtemp()
 
-        lr_model = LogisticRegressionWithSGD.train(rdd)
+        lr_model = LogisticRegressionWithSGD.train(rdd, iterations=10)
         self.assertTrue(lr_model.predict(features[0]) <= 0)
         self.assertTrue(lr_model.predict(features[1]) > 0)
         self.assertTrue(lr_model.predict(features[2]) <= 0)
         self.assertTrue(lr_model.predict(features[3]) > 0)
 
-        svm_model = SVMWithSGD.train(rdd)
+        svm_model = SVMWithSGD.train(rdd, iterations=10)
         self.assertTrue(svm_model.predict(features[0]) <= 0)
         self.assertTrue(svm_model.predict(features[1]) > 0)
         self.assertTrue(svm_model.predict(features[2]) <= 0)
@@ -291,7 +297,7 @@ def test_classification(self):
 
         categoricalFeaturesInfo = {0: 3}  # feature 0 has 3 categories
         dt_model = DecisionTree.trainClassifier(
-            rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo)
+            rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, maxBins=4)
         self.assertTrue(dt_model.predict(features[0]) <= 0)
         self.assertTrue(dt_model.predict(features[1]) > 0)
         self.assertTrue(dt_model.predict(features[2]) <= 0)
@@ -303,7 +309,8 @@ def test_classification(self):
         self.assertEqual(same_dt_model.toDebugString(), dt_model.toDebugString())
 
         rf_model = RandomForest.trainClassifier(
-            rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=100)
+            rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=10,
+            maxBins=4)
         self.assertTrue(rf_model.predict(features[0]) <= 0)
         self.assertTrue(rf_model.predict(features[1]) > 0)
         self.assertTrue(rf_model.predict(features[2]) <= 0)
@@ -315,7 +322,7 @@ def test_classification(self):
         self.assertEqual(same_rf_model.toDebugString(), rf_model.toDebugString())
 
         gbt_model = GradientBoostedTrees.trainClassifier(
-            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4)
         self.assertTrue(gbt_model.predict(features[0]) <= 0)
         self.assertTrue(gbt_model.predict(features[1]) > 0)
         self.assertTrue(gbt_model.predict(features[2]) <= 0)
@@ -344,19 +351,19 @@ def test_regression(self):
         rdd = self.sc.parallelize(data)
         features = [p.features.tolist() for p in data]
 
-        lr_model = LinearRegressionWithSGD.train(rdd)
+        lr_model = LinearRegressionWithSGD.train(rdd, iterations=10)
         self.assertTrue(lr_model.predict(features[0]) <= 0)
         self.assertTrue(lr_model.predict(features[1]) > 0)
         self.assertTrue(lr_model.predict(features[2]) <= 0)
         self.assertTrue(lr_model.predict(features[3]) > 0)
 
-        lasso_model = LassoWithSGD.train(rdd)
+        lasso_model = LassoWithSGD.train(rdd, iterations=10)
         self.assertTrue(lasso_model.predict(features[0]) <= 0)
         self.assertTrue(lasso_model.predict(features[1]) > 0)
         self.assertTrue(lasso_model.predict(features[2]) <= 0)
         self.assertTrue(lasso_model.predict(features[3]) > 0)
 
-        rr_model = RidgeRegressionWithSGD.train(rdd)
+        rr_model = RidgeRegressionWithSGD.train(rdd, iterations=10)
         self.assertTrue(rr_model.predict(features[0]) <= 0)
         self.assertTrue(rr_model.predict(features[1]) > 0)
         self.assertTrue(rr_model.predict(features[2]) <= 0)
@@ -364,35 +371,35 @@ def test_regression(self):
 
         categoricalFeaturesInfo = {0: 2}  # feature 0 has 2 categories
         dt_model = DecisionTree.trainRegressor(
-            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, maxBins=4)
         self.assertTrue(dt_model.predict(features[0]) <= 0)
         self.assertTrue(dt_model.predict(features[1]) > 0)
         self.assertTrue(dt_model.predict(features[2]) <= 0)
         self.assertTrue(dt_model.predict(features[3]) > 0)
 
         rf_model = RandomForest.trainRegressor(
-            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=100, seed=1)
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=10, maxBins=4, seed=1)
         self.assertTrue(rf_model.predict(features[0]) <= 0)
         self.assertTrue(rf_model.predict(features[1]) > 0)
         self.assertTrue(rf_model.predict(features[2]) <= 0)
         self.assertTrue(rf_model.predict(features[3]) > 0)
 
         gbt_model = GradientBoostedTrees.trainRegressor(
-            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
+            rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4)
         self.assertTrue(gbt_model.predict(features[0]) <= 0)
         self.assertTrue(gbt_model.predict(features[1]) > 0)
         self.assertTrue(gbt_model.predict(features[2]) <= 0)
         self.assertTrue(gbt_model.predict(features[3]) > 0)
 
         try:
-            LinearRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0]))
-            LassoWithSGD.train(rdd, initialWeights=array([1.0, 1.0]))
-            RidgeRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0]))
+            LinearRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10)
+            LassoWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10)
+            RidgeRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10)
         except ValueError:
             self.fail()
 
 
-class StatTests(PySparkTestCase):
+class StatTests(MLlibTestCase):
     # SPARK-4023
     def test_col_with_different_rdds(self):
         # numpy
@@ -422,7 +429,7 @@ def test_col_norms(self):
         self.assertTrue(math.fabs(summary2.normL2()[0] - expectedNormL2) < 1e-14)
 
 
-class VectorUDTTests(PySparkTestCase):
+class VectorUDTTests(MLlibTestCase):
 
     dv0 = DenseVector([])
     dv1 = DenseVector([1.0, 2.0])
@@ -456,7 +463,7 @@ def test_infer_schema(self):
 
 
 @unittest.skipIf(not _have_scipy, "SciPy not installed")
-class SciPyTests(PySparkTestCase):
+class SciPyTests(MLlibTestCase):
 
     """
     Test both vector operations and MLlib algorithms with SciPy sparse matrices,
@@ -597,7 +604,7 @@ def test_regression(self):
         self.assertTrue(dt_model.predict(features[3]) > 0)
 
 
-class ChiSqTestTests(PySparkTestCase):
+class ChiSqTestTests(MLlibTestCase):
     def test_goodness_of_fit(self):
         from numpy import inf
 
@@ -695,13 +702,13 @@ def test_right_number_of_results(self):
         self.assertIsNotNone(chi[1000])
 
 
-class SerDeTest(PySparkTestCase):
+class SerDeTest(MLlibTestCase):
     def test_to_java_object_rdd(self):  # SPARK-6660
         data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0)
         self.assertEqual(_to_java_object_rdd(data).count(), 10)
 
 
-class FeatureTest(PySparkTestCase):
+class FeatureTest(MLlibTestCase):
     def test_idf_model(self):
         data = [
             Vectors.dense([1, 2, 6, 0, 2, 3, 1, 1, 0, 0, 3]),
@@ -714,13 +721,8 @@ def test_idf_model(self):
         self.assertEqual(len(idf), 11)
 
 
-class Word2VecTests(PySparkTestCase):
+class Word2VecTests(MLlibTestCase):
     def test_word2vec_setters(self):
-        data = [
-            ["I", "have", "a", "pen"],
-            ["I", "like", "soccer", "very", "much"],
-            ["I", "live", "in", "Tokyo"]
-        ]
         model = Word2Vec() \
             .setVectorSize(2) \
             .setLearningRate(0.01) \
@@ -749,7 +751,7 @@ def test_word2vec_get_vectors(self):
         self.assertEquals(len(model.getVectors()), 3)
 
 
-class StandardScalerTests(PySparkTestCase):
+class StandardScalerTests(MLlibTestCase):
     def test_model_setters(self):
         data = [
             [1.0, 2.0, 3.0],
@@ -777,3 +779,4 @@ def test_model_transform(self):
     unittest.main()
     if not _have_scipy:
         print("NOTE: SciPy tests were skipped as it does not seem to be installed")
+    sc.stop()
diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
index 0fe6e4fabe43a..cfcbea573fd22 100644
--- a/python/pyspark/mllib/tree.py
+++ b/python/pyspark/mllib/tree.py
@@ -482,13 +482,13 @@ def trainClassifier(cls, data, categoricalFeaturesInfo,
         ...     LabeledPoint(1.0, [3.0])
         ... ]
         >>>
-        >>> model = GradientBoostedTrees.trainClassifier(sc.parallelize(data), {})
+        >>> model = GradientBoostedTrees.trainClassifier(sc.parallelize(data), {}, numIterations=10)
         >>> model.numTrees()
-        100
+        10
         >>> model.totalNumNodes()
-        300
+        30
         >>> print(model)  # it already has newline
-        TreeEnsembleModel classifier with 100 trees
+        TreeEnsembleModel classifier with 10 trees
         <BLANKLINE>
         >>> model.predict([2.0])
         1.0
@@ -541,11 +541,12 @@ def trainRegressor(cls, data, categoricalFeaturesInfo,
         ...     LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
         ... ]
         >>>
-        >>> model = GradientBoostedTrees.trainRegressor(sc.parallelize(sparse_data), {})
+        >>> data = sc.parallelize(sparse_data)
+        >>> model = GradientBoostedTrees.trainRegressor(data, {}, numIterations=10)
         >>> model.numTrees()
-        100
+        10
         >>> model.totalNumNodes()
-        102
+        12
         >>> model.predict(SparseVector(2, {1: 1.0}))
         1.0
         >>> model.predict(SparseVector(2, {0: 1.0}))
diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py
index b54baa57ec28a..1d0b16cade8bb 100644
--- a/python/pyspark/shuffle.py
+++ b/python/pyspark/shuffle.py
@@ -486,7 +486,7 @@ def sorted(self, iterator, key=None, reverse=False):
         goes above the limit.
         """
         global MemoryBytesSpilled, DiskBytesSpilled
-        batch, limit = 100, self._next_limit()
+        batch, limit = 100, self.memory_limit
         chunks, current_chunk = [], []
         iterator = iter(iterator)
         while True:
@@ -497,7 +497,7 @@ def sorted(self, iterator, key=None, reverse=False):
                 break
 
             used_memory = get_used_memory()
-            if used_memory > self.memory_limit:
+            if used_memory > limit:
                 # sort them inplace will save memory
                 current_chunk.sort(key=key, reverse=reverse)
                 path = self._get_path(len(chunks))
@@ -513,13 +513,14 @@ def load(f):
                 chunks.append(load(open(path, 'rb')))
                 current_chunk = []
                 gc.collect()
+                batch //= 2
                 limit = self._next_limit()
                 MemoryBytesSpilled += (used_memory - get_used_memory()) << 20
                 DiskBytesSpilled += os.path.getsize(path)
                 os.unlink(path)  # data will be deleted after close
 
             elif not chunks:
-                batch = min(batch * 2, 10000)
+                batch = min(int(batch * 1.5), 10000)
 
         current_chunk.sort(key=key, reverse=reverse)
         if not chunks:
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 23e84283679e1..fe43c374f1cb1 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -109,7 +109,7 @@ def setUpClass(cls):
         os.unlink(cls.tempdir.name)
         cls.sqlCtx = SQLContext(cls.sc)
         cls.testData = [Row(key=i, value=str(i)) for i in range(100)]
-        rdd = cls.sc.parallelize(cls.testData)
+        rdd = cls.sc.parallelize(cls.testData, 2)
         cls.df = rdd.toDF()
 
     @classmethod
@@ -303,7 +303,7 @@ def test_apply_schema(self):
         abstract = "byte1 short1 float1 time1 map1{} struct1(b) list1[]"
         schema = _parse_schema_abstract(abstract)
         typedSchema = _infer_schema_type(rdd.first(), schema)
-        df = self.sqlCtx.applySchema(rdd, typedSchema)
+        df = self.sqlCtx.createDataFrame(rdd, typedSchema)
         r = (127, -32768, 1.0, datetime(2010, 1, 1, 1, 1, 1), {"a": 1}, Row(b=2), [1, 2, 3])
         self.assertEqual(r, tuple(df.first()))
 
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 33f958a601f3a..5fa1e5ef081ab 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -16,14 +16,23 @@
 #
 
 import os
+import sys
 from itertools import chain
 import time
 import operator
-import unittest
 import tempfile
 import struct
 from functools import reduce
 
+if sys.version_info[:2] <= (2, 6):
+    try:
+        import unittest2 as unittest
+    except ImportError:
+        sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
+        sys.exit(1)
+else:
+    import unittest
+
 from pyspark.context import SparkConf, SparkContext, RDD
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.kafka import KafkaUtils
@@ -31,19 +40,25 @@
 
 class PySparkStreamingTestCase(unittest.TestCase):
 
-    timeout = 20  # seconds
-    duration = 1
+    timeout = 4  # seconds
+    duration = .2
 
-    def setUp(self):
-        class_name = self.__class__.__name__
+    @classmethod
+    def setUpClass(cls):
+        class_name = cls.__name__
         conf = SparkConf().set("spark.default.parallelism", 1)
-        self.sc = SparkContext(appName=class_name, conf=conf)
-        self.sc.setCheckpointDir("/tmp")
-        # TODO: decrease duration to speed up tests
+        cls.sc = SparkContext(appName=class_name, conf=conf)
+        cls.sc.setCheckpointDir("/tmp")
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.sc.stop()
+
+    def setUp(self):
         self.ssc = StreamingContext(self.sc, self.duration)
 
     def tearDown(self):
-        self.ssc.stop()
+        self.ssc.stop(False)
 
     def wait_for(self, result, n):
         start_time = time.time()
@@ -363,13 +378,13 @@ def func(dstream):
 
 class WindowFunctionTests(PySparkStreamingTestCase):
 
-    timeout = 20
+    timeout = 5
 
     def test_window(self):
         input = [range(1), range(2), range(3), range(4), range(5)]
 
         def func(dstream):
-            return dstream.window(3, 1).count()
+            return dstream.window(.6, .2).count()
 
         expected = [[1], [3], [6], [9], [12], [9], [5]]
         self._test_func(input, func, expected)
@@ -378,7 +393,7 @@ def test_count_by_window(self):
         input = [range(1), range(2), range(3), range(4), range(5)]
 
         def func(dstream):
-            return dstream.countByWindow(3, 1)
+            return dstream.countByWindow(.6, .2)
 
         expected = [[1], [3], [6], [9], [12], [9], [5]]
         self._test_func(input, func, expected)
@@ -387,7 +402,7 @@ def test_count_by_window_large(self):
         input = [range(1), range(2), range(3), range(4), range(5), range(6)]
 
         def func(dstream):
-            return dstream.countByWindow(5, 1)
+            return dstream.countByWindow(1, .2)
 
         expected = [[1], [3], [6], [10], [15], [20], [18], [15], [11], [6]]
         self._test_func(input, func, expected)
@@ -396,7 +411,7 @@ def test_count_by_value_and_window(self):
         input = [range(1), range(2), range(3), range(4), range(5), range(6)]
 
         def func(dstream):
-            return dstream.countByValueAndWindow(5, 1)
+            return dstream.countByValueAndWindow(1, .2)
 
         expected = [[1], [2], [3], [4], [5], [6], [6], [6], [6], [6]]
         self._test_func(input, func, expected)
@@ -405,7 +420,7 @@ def test_group_by_key_and_window(self):
         input = [[('a', i)] for i in range(5)]
 
         def func(dstream):
-            return dstream.groupByKeyAndWindow(3, 1).mapValues(list)
+            return dstream.groupByKeyAndWindow(.6, .2).mapValues(list)
 
         expected = [[('a', [0])], [('a', [0, 1])], [('a', [0, 1, 2])], [('a', [1, 2, 3])],
                     [('a', [2, 3, 4])], [('a', [3, 4])], [('a', [4])]]
@@ -436,8 +451,8 @@ def test_stop_only_streaming_context(self):
     def test_stop_multiple_times(self):
         self._add_input_stream()
         self.ssc.start()
-        self.ssc.stop()
-        self.ssc.stop()
+        self.ssc.stop(False)
+        self.ssc.stop(False)
 
     def test_queue_stream(self):
         input = [list(range(i + 1)) for i in range(3)]
@@ -495,10 +510,7 @@ def func(rdds):
         self.assertEqual([2, 3, 1], self._take(dstream, 3))
 
 
-class CheckpointTests(PySparkStreamingTestCase):
-
-    def setUp(self):
-        pass
+class CheckpointTests(unittest.TestCase):
 
     def test_get_or_create(self):
         inputd = tempfile.mkdtemp()
@@ -518,12 +530,12 @@ def setup():
             return ssc
 
         cpd = tempfile.mkdtemp("test_streaming_cps")
-        self.ssc = ssc = StreamingContext.getOrCreate(cpd, setup)
+        ssc = StreamingContext.getOrCreate(cpd, setup)
         ssc.start()
 
         def check_output(n):
             while not os.listdir(outputd):
-                time.sleep(0.1)
+                time.sleep(0.01)
             time.sleep(1)  # make sure mtime is larger than the previous one
             with open(os.path.join(inputd, str(n)), 'w') as f:
                 f.writelines(["%d\n" % i for i in range(10)])
@@ -553,12 +565,15 @@ def check_output(n):
         ssc.stop(True, True)
 
         time.sleep(1)
-        self.ssc = ssc = StreamingContext.getOrCreate(cpd, setup)
+        ssc = StreamingContext.getOrCreate(cpd, setup)
         ssc.start()
         check_output(3)
+        ssc.stop(True, True)
 
 
 class KafkaStreamTests(PySparkStreamingTestCase):
+    timeout = 20  # seconds
+    duration = 1
 
     def setUp(self):
         super(KafkaStreamTests, self).setUp()
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 75f39d9e75f38..ea63a396da5b8 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -31,7 +31,6 @@
 import time
 import zipfile
 import random
-import itertools
 import threading
 import hashlib
 
@@ -49,6 +48,11 @@
         xrange = range
         basestring = str
 
+if sys.version >= "3":
+    from io import StringIO
+else:
+    from StringIO import StringIO
+
 
 from pyspark.conf import SparkConf
 from pyspark.context import SparkContext
@@ -196,7 +200,7 @@ def test_external_sort_in_rdd(self):
         sc = SparkContext(conf=conf)
         l = list(range(10240))
         random.shuffle(l)
-        rdd = sc.parallelize(l, 2)
+        rdd = sc.parallelize(l, 4)
         self.assertEqual(sorted(l), rdd.sortBy(lambda x: x).collect())
         sc.stop()
 
@@ -300,6 +304,18 @@ def test_hash_serializer(self):
         hash(FlattenedValuesSerializer(PickleSerializer()))
 
 
+class QuietTest(object):
+    def __init__(self, sc):
+        self.log4j = sc._jvm.org.apache.log4j
+
+    def __enter__(self):
+        self.old_level = self.log4j.LogManager.getRootLogger().getLevel()
+        self.log4j.LogManager.getRootLogger().setLevel(self.log4j.Level.FATAL)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.log4j.LogManager.getRootLogger().setLevel(self.old_level)
+
+
 class PySparkTestCase(unittest.TestCase):
 
     def setUp(self):
@@ -371,15 +387,11 @@ def test_add_py_file(self):
         # To ensure that we're actually testing addPyFile's effects, check that
         # this job fails due to `userlibrary` not being on the Python path:
         # disable logging in log4j temporarily
-        log4j = self.sc._jvm.org.apache.log4j
-        old_level = log4j.LogManager.getRootLogger().getLevel()
-        log4j.LogManager.getRootLogger().setLevel(log4j.Level.FATAL)
-
         def func(x):
             from userlibrary import UserClass
             return UserClass().hello()
-        self.assertRaises(Exception, self.sc.parallelize(range(2)).map(func).first)
-        log4j.LogManager.getRootLogger().setLevel(old_level)
+        with QuietTest(self.sc):
+            self.assertRaises(Exception, self.sc.parallelize(range(2)).map(func).first)
 
         # Add the file, so the job should now succeed:
         path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py")
@@ -496,7 +508,8 @@ def test_deleting_input_files(self):
         filtered_data = data.filter(lambda x: True)
         self.assertEqual(1, filtered_data.count())
         os.unlink(tempFile.name)
-        self.assertRaises(Exception, lambda: filtered_data.count())
+        with QuietTest(self.sc):
+            self.assertRaises(Exception, lambda: filtered_data.count())
 
     def test_sampling_default_seed(self):
         # Test for SPARK-3995 (default seed setting)
@@ -536,9 +549,9 @@ def test_namedtuple_in_rdd(self):
         self.assertEqual([jon, jane], theDoes.collect())
 
     def test_large_broadcast(self):
-        N = 100000
+        N = 10000
         data = [[float(i) for i in range(300)] for i in range(N)]
-        bdata = self.sc.broadcast(data)  # 270MB
+        bdata = self.sc.broadcast(data)  # 27MB
         m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum()
         self.assertEqual(N, m)
 
@@ -569,7 +582,7 @@ def test_multiple_broadcasts(self):
         self.assertEqual(checksum, csum)
 
     def test_large_closure(self):
-        N = 1000000
+        N = 200000
         data = [float(i) for i in xrange(N)]
         rdd = self.sc.parallelize(range(1), 1).map(lambda x: len(data))
         self.assertEqual(N, rdd.first())
@@ -604,17 +617,18 @@ def test_zip_with_different_number_of_items(self):
         # different number of partitions
         b = self.sc.parallelize(range(100, 106), 3)
         self.assertRaises(ValueError, lambda: a.zip(b))
-        # different number of batched items in JVM
-        b = self.sc.parallelize(range(100, 104), 2)
-        self.assertRaises(Exception, lambda: a.zip(b).count())
-        # different number of items in one pair
-        b = self.sc.parallelize(range(100, 106), 2)
-        self.assertRaises(Exception, lambda: a.zip(b).count())
-        # same total number of items, but different distributions
-        a = self.sc.parallelize([2, 3], 2).flatMap(range)
-        b = self.sc.parallelize([3, 2], 2).flatMap(range)
-        self.assertEqual(a.count(), b.count())
-        self.assertRaises(Exception, lambda: a.zip(b).count())
+        with QuietTest(self.sc):
+            # different number of batched items in JVM
+            b = self.sc.parallelize(range(100, 104), 2)
+            self.assertRaises(Exception, lambda: a.zip(b).count())
+            # different number of items in one pair
+            b = self.sc.parallelize(range(100, 106), 2)
+            self.assertRaises(Exception, lambda: a.zip(b).count())
+            # same total number of items, but different distributions
+            a = self.sc.parallelize([2, 3], 2).flatMap(range)
+            b = self.sc.parallelize([3, 2], 2).flatMap(range)
+            self.assertEqual(a.count(), b.count())
+            self.assertRaises(Exception, lambda: a.zip(b).count())
 
     def test_count_approx_distinct(self):
         rdd = self.sc.parallelize(range(1000))
@@ -877,7 +891,12 @@ def test_profiler(self):
         func_names = [func_name for fname, n, func_name in stat_list]
         self.assertTrue("heavy_foo" in func_names)
 
+        old_stdout = sys.stdout
+        sys.stdout = io = StringIO()
         self.sc.show_profiles()
+        self.assertTrue("heavy_foo" in io.getvalue())
+        sys.stdout = old_stdout
+
         d = tempfile.gettempdir()
         self.sc.dump_profiles(d)
         self.assertTrue("rdd_%d.pstats" % id in os.listdir(d))
@@ -901,7 +920,7 @@ def show(self, id):
 
     def do_computation(self):
         def heavy_foo(x):
-            for i in range(1 << 20):
+            for i in range(1 << 18):
                 x = 1
 
         rdd = self.sc.parallelize(range(100))
@@ -1417,7 +1436,7 @@ def test_termination_sigterm(self):
         self.do_termination_test(lambda daemon: os.kill(daemon.pid, SIGTERM))
 
 
-class WorkerTests(PySparkTestCase):
+class WorkerTests(ReusedPySparkTestCase):
     def test_cancel_task(self):
         temp = tempfile.NamedTemporaryFile(delete=True)
         temp.close()
@@ -1432,7 +1451,10 @@ def sleep(x):
 
         # start job in background thread
         def run():
-            self.sc.parallelize(range(1), 1).foreach(sleep)
+            try:
+                self.sc.parallelize(range(1), 1).foreach(sleep)
+            except Exception:
+                pass
         import threading
         t = threading.Thread(target=run)
         t.daemon = True
@@ -1473,7 +1495,8 @@ def test_after_exception(self):
         def raise_exception(_):
             raise Exception()
         rdd = self.sc.parallelize(range(100), 1)
-        self.assertRaises(Exception, lambda: rdd.foreach(raise_exception))
+        with QuietTest(self.sc):
+            self.assertRaises(Exception, lambda: rdd.foreach(raise_exception))
         self.assertEqual(100, rdd.map(str).count())
 
     def test_after_jvm_exception(self):
@@ -1484,7 +1507,8 @@ def test_after_jvm_exception(self):
         filtered_data = data.filter(lambda x: True)
         self.assertEqual(1, filtered_data.count())
         os.unlink(tempFile.name)
-        self.assertRaises(Exception, lambda: filtered_data.count())
+        with QuietTest(self.sc):
+            self.assertRaises(Exception, lambda: filtered_data.count())
 
         rdd = self.sc.parallelize(range(100), 1)
         self.assertEqual(100, rdd.map(str).count())
@@ -1522,14 +1546,11 @@ def test_with_different_versions_of_python(self):
         rdd.count()
         version = sys.version_info
         sys.version_info = (2, 0, 0)
-        log4j = self.sc._jvm.org.apache.log4j
-        old_level = log4j.LogManager.getRootLogger().getLevel()
-        log4j.LogManager.getRootLogger().setLevel(log4j.Level.FATAL)
         try:
-            self.assertRaises(Py4JJavaError, lambda: rdd.count())
+            with QuietTest(self.sc):
+                self.assertRaises(Py4JJavaError, lambda: rdd.count())
         finally:
             sys.version_info = version
-            log4j.LogManager.getRootLogger().setLevel(old_level)
 
 
 class SparkSubmitTests(unittest.TestCase):
@@ -1751,9 +1772,14 @@ def test_with_stop(self):
     def test_progress_api(self):
         with SparkContext() as sc:
             sc.setJobGroup('test_progress_api', '', True)
-
             rdd = sc.parallelize(range(10)).map(lambda x: time.sleep(100))
-            t = threading.Thread(target=rdd.collect)
+
+            def run():
+                try:
+                    rdd.count()
+                except Exception:
+                    pass
+            t = threading.Thread(target=run)
             t.daemon = True
             t.start()
             # wait for scheduler to start
diff --git a/python/run-tests b/python/run-tests
index ed3e819ef30c1..88b63b84fdc27 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -28,6 +28,7 @@ cd "$FWDIR/python"
 
 FAILED=0
 LOG_FILE=unit-tests.log
+START=$(date +"%s")
 
 rm -f $LOG_FILE
 
@@ -35,8 +36,8 @@ rm -f $LOG_FILE
 rm -rf metastore warehouse
 
 function run_test() {
-    echo "Running test: $1" | tee -a $LOG_FILE
-
+    echo -en "Running test: $1 ... " | tee -a $LOG_FILE
+    start=$(date +"%s")
     SPARK_TESTING=1 time "$FWDIR"/bin/pyspark $1 > $LOG_FILE 2>&1
 
     FAILED=$((PIPESTATUS[0]||$FAILED))
@@ -48,6 +49,9 @@ function run_test() {
         echo "Had test failures; see logs."
         echo -en "\033[0m"  # No color
         exit -1
+    else
+        now=$(date +"%s")
+        echo "ok ($(($now - $start))s)"
     fi
 }
 
@@ -161,9 +165,8 @@ if [ $(which pypy) ]; then
 fi
 
 if [[ $FAILED == 0 ]]; then
-    echo -en "\033[32m"  # Green
-    echo "Tests passed."
-    echo -en "\033[0m"  # No color
+    now=$(date +"%s")
+    echo -e "\033[32mTests passed \033[0min $(($now - $START)) seconds"
 fi
 
 # TODO: in the long-run, it would be nice to use a test runner like `nose`.