diff --git a/pgl/tests/test_dataloader.py b/pgl/tests/test_dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3323903c8e600ea8ce11767a35e44059c20862d
--- /dev/null
+++ b/pgl/tests/test_dataloader.py
@@ -0,0 +1,146 @@
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""test_dataloader"""
+
+import time
+import unittest
+import json
+import os
+
+from pgl.utils.data.dataset import Dataset, StreamDataset
+from pgl.utils.data.dataloader import Dataloader
+
+DATA_SIZE = 20
+
+
+class ListDataset(Dataset):
+    def __init__(self):
+        self.dataset = list(range(0, DATA_SIZE))
+
+    def __getitem__(self, idx):
+        return self._transform(self.dataset[idx])
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def _transform(self, example):
+        time.sleep(0.1)
+        return example
+
+
+class IterDataset(StreamDataset):
+    def __init__(self):
+        self.dataset = list(range(0, DATA_SIZE))
+        self.count = 0
+
+    def __iter__(self):
+        for data in self.dataset:
+            self.count += 1
+            if self.count % self._worker_info.num_workers != self._worker_info.fid:
+                continue
+            time.sleep(0.1)
+            yield data
+
+
+class Collate_fn(object):
+    def __init__(self, config):
+        self.config = config
+
+    def __call__(self, batch_examples):
+        feed_dict = {}
+        feed_dict['data'] = batch_examples
+        feed_dict['labels'] = [i for i in range(len(batch_examples))]
+        return feed_dict
+
+
+class DataloaderTest(unittest.TestCase):
+    def test_ListDataset(self):
+        config = {
+            'batch_size': 3,
+            'drop_last': True,
+            'shuffle': True,
+            'num_workers': 2,
+        }
+        collate_fn = Collate_fn(config)
+        ds = ListDataset()
+
+        # test batch_size
+        loader = Dataloader(
+            ds,
+            batch_size=config['batch_size'],
+            drop_last=config['drop_last'],
+            num_workers=config['num_workers'],
+            collate_fn=collate_fn)
+
+        epochs = 1
+        for e in range(epochs):
+            res = []
+            for batch_data in loader:
+                res.extend(batch_data['data'])
+                self.assertEqual(len(batch_data['data']), config['batch_size'])
+
+        # test shuffle
+        loader = Dataloader(
+            ds,
+            batch_size=3,
+            drop_last=False,
+            shuffle=True,
+            num_workers=1,
+            collate_fn=collate_fn)
+
+        for e in range(epochs):
+            res = []
+            for batch_data in loader:
+                res.extend(batch_data['data'])
+            self.assertEqual(set([i for i in range(DATA_SIZE)]), set(res))
+
+    def test_IterDataset(self):
+        config = {
+            'batch_size': 3,
+            'drop_last': True,
+            'num_workers': 2,
+        }
+        collate_fn = Collate_fn(config)
+        ds = IterDataset()
+        loader = Dataloader(
+            ds,
+            batch_size=config['batch_size'],
+            drop_last=config['drop_last'],
+            num_workers=config['num_workers'],
+            collate_fn=collate_fn)
+
+        epochs = 1
+        for e in range(epochs):
+            res = []
+            for batch_data in loader:
+                res.extend(batch_data['data'])
+                self.assertEqual(len(batch_data['data']), config['batch_size'])
+
+        # test shuffle
+        loader = Dataloader(
+            ds,
+            batch_size=3,
+            drop_last=False,
+            num_workers=1,
+            collate_fn=collate_fn)
+
+        for e in range(epochs):
+            res = []
+            for batch_data in loader:
+                res.extend(batch_data['data'])
+            self.assertEqual(set([i for i in range(DATA_SIZE)]), set(res))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/pgl/utils/data/__init__.py b/pgl/utils/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..54cf127c22dc74c34b620f9eacb8a2a10f484f68
--- /dev/null
+++ b/pgl/utils/data/__init__.py
@@ -0,0 +1,3 @@
+#-*- coding: utf-8 -*-
+from .dataset import Dataset, StreamDataset
+from .dataloader import Dataloader
diff --git a/pgl/utils/data/dataloader.py b/pgl/utils/data/dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..4100bc89ef9674d155aa27cefee8e80bec6759bb
--- /dev/null
+++ b/pgl/utils/data/dataloader.py
@@ -0,0 +1,252 @@
+#-*- coding: utf-8 -*-
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""dataloader
+"""
+import warnings
+import numpy as np
+from collections import namedtuple
+
+import paddle
+import paddle.fluid as F
+import paddle.fluid.layers as L
+
+from pgl.utils import mp_reader
+from pgl.utils.data.dataset import Dataset, StreamDataset
+from pgl.utils.data.sampler import Sampler, StreamSampler
+
+WorkerInfo = namedtuple("WorkerInfo", ["num_workers", "fid"])
+
+
+class Dataloader(object):
+    """Dataloader for loading batch data
+
+    Example:
+        .. code-block:: python
+            from pgl.utils.data import Dataset
+            from pgl.utils.data.dataloader import Dataloader
+
+            class MyDataset(Dataset):
+                def __init__(self):
+                    self.data = list(range(0, 40))
+
+                def __getitem__(self, idx):
+                    return self.data[idx]
+
+                def __len__(self):
+                    return len(self.data)
+
+            def collate_fn(batch_examples):
+                feed_dict = {}
+                feed_dict['data'] = batch_examples
+                return feed_dict
+
+            dataset = MyDataset()
+            loader = Dataloader(dataset, 
+                                batch_size=3,
+                                drop_last=False,
+                                shuffle=True,
+                                num_workers=4,
+                                collate_fn=collate_fn)
+
+            for batch_data in loader:
+                print(batch_data)
+    """
+
+    def __init__(self,
+                 dataset,
+                 batch_size=1,
+                 drop_last=False,
+                 shuffle=False,
+                 num_workers=1,
+                 collate_fn=None,
+                 buf_size=1000,
+                 stream_shuffle_size=0):
+
+        self.dataset = dataset
+        self.batch_size = batch_size
+        self.shuffle = shuffle
+        self.num_workers = num_workers
+        self.collate_fn = collate_fn
+        self.buf_size = buf_size
+        self.drop_last = drop_last
+        self.stream_shuffle_size = stream_shuffle_size
+
+        if self.shuffle and isinstance(self.dataset, StreamDataset):
+            warn_msg = "[shuffle] should not be True with StreamDataset. " \
+                    "It will be ignored. " \
+                    "You might want to set [stream_shuffle_size] with StreamDataset."
+            warnings.warn(warn_msg)
+
+        if self.stream_shuffle_size > 0 and self.batch_size >= stream_shuffle_size:
+            raise ValueError("stream_shuffle_size must be larger than batch_size," \
+                    "but got [stream_shuffle_size=%s] smaller than [batch_size=%s]" \
+                    % (self.stream_shuffle_size, self.batch_size))
+
+        if self.num_workers < 1:
+            raise ValueError("num_workers(default: 1) should be larger than 0, " \
+                        "but got [num_workers=%s] < 1." % self.num_workers)
+
+    def __len__(self):
+        if not isinstance(self.dataset, StreamDataset):
+            return len(self.sampler)
+        else:
+            raise "StreamDataset has no length"
+
+    def __iter__(self):
+        # generating a iterable sequence for produce batch data without repetition
+        if isinstance(self.dataset, StreamDataset):  # for stream data
+            self.sampler = StreamSampler(
+                self.dataset,
+                batch_size=self.batch_size,
+                drop_last=self.drop_last)
+        else:
+            self.sampler = Sampler(
+                self.dataset,
+                batch_size=self.batch_size,
+                drop_last=self.drop_last,
+                shuffle=self.shuffle)
+
+        if self.num_workers == 1:
+            r = paddle.reader.buffered(_DataLoaderIter(self, 0), self.buf_size)
+        else:
+            worker_pool = [
+                _DataLoaderIter(self, wid) for wid in range(self.num_workers)
+            ]
+            workers = mp_reader.multiprocess_reader(
+                worker_pool, use_pipe=True, queue_size=1000)
+            r = paddle.reader.buffered(workers, self.buf_size)
+
+        for batch in r():
+            yield batch
+
+    def __call__(self):
+        return self.__iter__()
+
+
+class _DataLoaderIter(object):
+    """Iterable DataLoader Object
+    """
+
+    def __init__(self, dataloader, fid=0):
+        self.dataset = dataloader.dataset
+        self.sampler = dataloader.sampler
+        self.collate_fn = dataloader.collate_fn
+        self.num_workers = dataloader.num_workers
+        self.drop_last = dataloader.drop_last
+        self.batch_size = dataloader.batch_size
+        self.stream_shuffle_size = dataloader.stream_shuffle_size
+        self.fid = fid
+        self.count = 0
+
+    def _data_generator(self):
+        for indices in self.sampler:
+
+            self.count += 1
+            if self.count % self.num_workers != self.fid:
+                continue
+
+            batch_data = [self.dataset[i] for i in indices]
+
+            if self.collate_fn is not None:
+                yield self.collate_fn(batch_data)
+            else:
+                yield batch_data
+
+    def _streamdata_generator(self):
+        self._worker_info = WorkerInfo(
+            num_workers=self.num_workers, fid=self.fid)
+        self.dataset._set_worker_info(self._worker_info)
+
+        dataset = iter(self.dataset)
+        for indices in self.sampler:
+            batch_data = []
+            for _ in indices:
+                try:
+                    batch_data.append(next(dataset))
+                except StopIteration:
+                    break
+
+            if len(batch_data) == 0 or (self.drop_last and
+                                        len(batch_data) < len(indices)):
+                break
+                #  raise StopIteration
+
+            # make sure do not repeat in multiprocessing 
+            self.count += 1
+
+            if self.collate_fn is not None:
+                yield self.collate_fn(batch_data)
+            else:
+                yield batch_data
+
+    def _stream_shuffle_data_generator(self):
+        def _stream_shuffle_index_generator():
+            shuffle_size = [i for i in range(self.stream_shuffle_size)]
+            while True:
+                yield shuffle_size
+
+        def _data_generator():
+            dataset = iter(self.dataset)
+            for shuffle_size in _stream_shuffle_index_generator():
+                shuffle_size_data = []
+                for idx in shuffle_size:
+                    try:
+                        shuffle_size_data.append(next(dataset))
+                    except StopIteration:
+                        break
+
+                if len(shuffle_size_data) == 0:
+                    break
+
+                yield shuffle_size_data
+
+        def _batch_data_generator():
+            batch_data = []
+            for shuffle_size_data in _data_generator():
+                np.random.shuffle(shuffle_size_data)
+
+                for d in shuffle_size_data:
+                    batch_data.append(d)
+                    if len(batch_data) == self.batch_size:
+                        yield batch_data
+                        batch_data = []
+
+            if not self.drop_last and len(batch_data) > 0:
+                yield batch_data
+
+        self._worker_info = WorkerInfo(
+            num_workers=self.num_workers, fid=self.fid)
+        self.dataset._set_worker_info(self._worker_info)
+
+        for batch_data in _batch_data_generator():
+            if self.collate_fn is not None:
+                yield self.collate_fn(batch_data)
+            else:
+                yield batch_data
+
+    def __iter__(self):
+        if isinstance(self.dataset, StreamDataset):
+            if self.stream_shuffle_size > 0:
+                data_generator = self._stream_shuffle_data_generator
+            else:
+                data_generator = self._streamdata_generator
+        else:
+            data_generator = self._data_generator
+
+        for batch_data in data_generator():
+            yield batch_data
+
+    def __call__(self):
+        return self.__iter__()
diff --git a/pgl/utils/data/dataset.py b/pgl/utils/data/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..184bb555388e58283435ab6e00058f699ddb12c8
--- /dev/null
+++ b/pgl/utils/data/dataset.py
@@ -0,0 +1,151 @@
+#-*- coding: utf-8 -*-
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""dataset
+"""
+
+import os
+import sys
+import numpy as np
+import json
+import io
+from subprocess import Popen, PIPE
+
+
+class HadoopUtil(object):
+    """Implementation of some common hadoop operations.
+
+    """
+
+    def __init__(self, hadoop_bin, fs_name, fs_ugi):
+        self.hadoop_bin = hadoop_bin
+        self.fs_name = fs_name
+        self.fs_ugi = fs_ugi
+
+    def ls(self, path):
+        """ hdfs_ls """
+        cmd = self.hadoop_bin + " fs -D fs.default.name=" + self.fs_name
+        cmd += " -D hadoop.job.ugi=" + self.fs_ugi
+        cmd += " -ls " + path
+        cmd += " | grep part | awk '{print $8}'"
+        with os.popen(cmd) as reader:
+            filelist = reader.read().split()
+        return filelist
+
+    def open(self, filename, encoding='utf-8'):
+        """ hdfs_file_open """
+        cmd = self.hadoop_bin + " fs -D fs.default.name=" + self.fs_name
+        cmd += " -D hadoop.job.ugi=" + self.fs_ugi
+        cmd += " -cat " + filename
+
+        p = Popen(cmd, shell=True, stdout=PIPE)
+        p = io.TextIOWrapper(p.stdout, encoding=encoding, errors='ignore')
+        return p
+
+
+class Dataset(object):
+    """An abstract class represening Dataset.
+    Generally, all datasets should subclass it.
+    All subclasses should overwrite :code:`__getitem__` and :code:`__len__`.
+
+    Examples:
+        .. code-block:: python
+
+            from pgl.utils.data import Dataset
+
+            class MyDataset(Dataset):
+                def __init__(self):
+                    self.data = list(range(0, 40))
+
+                def __getitem__(self, idx):
+                    return self.data[idx]
+
+                def __len__(self):
+                    return len(self.data)
+    """
+
+    def __len__(self):
+        raise NotImplementedError
+
+    def __getitem__(self, idx):
+        raise NotImplementedError
+
+
+class StreamDataset(object):
+    """An abstract class represening StreamDataset which has unknown length.
+    Generally, all unknown length datasets should subclass it.
+    All subclasses should overwrite :code:`__iter__`.
+
+    Examples:
+        .. code-block:: python
+
+            from pgl.utils.data import StreamDataset
+
+            class MyStreamDataset(StreamDataset):
+                def __init__(self):
+                    self.data = list(range(0, 40))
+                    self.count = 0
+
+                def __iter__(self):
+                     for data in self.dataset:
+                        self.count += 1
+                        if self.count % self._worker_info.num_workers != self._worker_info.fid:
+                            continue
+                        # do something (like parse data)  of your data
+                        time.sleep(0.1)
+                        yield data
+    """
+
+    def __iter__(self):
+        raise NotImplementedError
+
+    def _set_worker_info(self, worker_info):
+        self._worker_info = worker_info
+
+
+class HadoopDataset(StreamDataset):
+    """An abstract class represening HadoopDataset which loads data from hdfs.
+    All subclasses should overwrite :code:`__iter__`.
+
+    Examples:
+        .. code-block:: python
+
+            from pgl.utils.data import HadoopDataset
+
+            class MyHadoopDataset(HadoopDataset):
+                def __init__(self, data_path, hadoop_bin, fs_name, fs_ugi):
+                    super(MyHadoopDataset, self).__init__(hadoop_bin, fs_name, fs_ugi)
+                    self.data_path = data_path
+
+                def __iter__(self):
+                    for line in self._line_data_generator():
+                        yield line    
+
+                def _line_data_generator(self):
+                    paths = self.hadoop_util.ls(self.data_path)
+                    paths = sorted(paths)
+                    for idx, filename in enumerate(paths):
+                        if idx % self._worker_info.num_workers != self._worker_info.fid:
+                            continue
+                        with self.hadoop_util.open(filename) as f:
+                            for line in f:
+                                yield line
+    """
+
+    def __init__(self, hadoop_bin, fs_name, fs_ugi):
+        self.hadoop_util = HadoopUtil(
+            hadoop_bin=hadoop_bin, fs_name=fs_name, fs_ugi=fs_ugi)
+
+    def __iter__(self):
+        raise NotImplementedError
diff --git a/pgl/utils/data/sampler.py b/pgl/utils/data/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cfd2bf430e340d5d093733812db2cd492a72ab0
--- /dev/null
+++ b/pgl/utils/data/sampler.py
@@ -0,0 +1,67 @@
+#-*- coding: utf-8 -*-
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""sampler
+"""
+
+import time
+import numpy as np
+
+
+class Sampler(object):
+    def __init__(self, dataset, batch_size=1, drop_last=False, shuffle=False):
+        self.dataset = dataset
+        self.batch_size = batch_size
+        self.drop_last = drop_last
+        self.shuffle = shuffle
+
+        length = len(self.dataset)
+        self.perm = np.arange(0, length)
+
+        # shuffle one time whne Sampler is created
+        if self.shuffle:
+            seed = int(float(time.time()) * 1000) % 10000007
+            np.random.seed(seed)
+            np.random.shuffle(self.perm)
+
+    def __iter__(self):
+        batch = []
+        for idx in self.perm:
+            batch.append(idx)
+            if len(batch) == self.batch_size:
+                yield batch
+                batch = []
+
+        if len(batch) > 0 and not self.drop_last:
+            yield batch
+
+    def __len__(self):
+        length = len(self.dataset)
+        if self.drop_last:
+            length = length // self.batch_size
+        else:
+            length = (length + self.batch_size - 1) // self.batch_size
+        return length
+
+
+class StreamSampler(object):
+    def __init__(self, dataset, batch_size=1, drop_last=None):
+        self.dataset = dataset
+        self.batch_size = batch_size
+        self.drop_last = drop_last
+
+    def __iter__(self):
+        batch = [i for i in range(self.batch_size)]
+        while True:
+            yield batch