Hadoop 3.0以及未来 刘 轶 自我简介 • Apache Hadoop的committer和顷目管理委员会成员。 • ebay的Paid IM(互联网市场)部门架构师，领导ebay产品广告、互 联网市场数据和实验平台的架构设计。负责领导使用Hadoop、 Spark、Kafka、Cassandra等开源大数据顷目建立ebay的广告和数 据平台。 • 加入ebay前，在intel工作6年，大数据架构师，负责领导大数据的 MapReduce Paper HBase Hive Cloudera创立 Hortonworks创立 Hadoop 1.0发布 Hadoop 2.0 GA Spark成为顶级顷目 Hadoop 3.0 2017 Hadoop生态系统 文件存储层 HDFS 资源／任务调度 YARN 计算引擎MapReduce 计算引擎Spark NoSQL HBase 数据仓 库SQL 机器/深 度学习

PAX Exam to write integration tests when developing applications using Karaf. Starting with Karaf 3.0 we've also included a component briding between Karaf and Pax Exam making it easier to write integration Exception { assertTrue(true); } } COMMANDS Basically the Pax Exam - Karaf bridge introduced with 3.0 should support all commands you know from Pax Exam 2.x. In addition we've added various additional

__git_version__ will return git commit sha of current build (GH21295). • Compatibility with Matplotlib 3.0 (GH22790). • Added Interval.overlaps(), arrays.IntervalArray.overlaps(), and IntervalIndex. overlaps() default integer index: In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a 590397 5 1.0 2013-01-03 -0.238464 -0.486944 0.015596 5 2.0 2013-01-04 -0.274925 0.823338 -0.761681 5 3.0 2013-01-05 1.092525 1.164866 -0.846108 5 4.0 2013-01-06 1.001105 0.071785 -1.067411 5 5.0 A where

default integer index: In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a 226064 5 1.0 2013-01-03 -0.136964 -0.276600 -0.614256 5 2.0 2013-01-04 0.066430 0.886690 1.544564 5 3.0 2013-01-05 0.996132 0.368752 1.232876 5 4.0 2013-01-06 -0.827664 0.620576 -0.247042 5 5.0 A where -1.0 2013-01-03 -0.136964 -0.276600 -0.614256 -5 -2.0 2013-01-04 -0.066430 -0.886690 -1.544564 -5 -3.0 2013-01-05 -0.996132 -0.368752 -1.232876 -5 -4.0 2013-01-06 -0.827664 -0.620576 -0.247042 -5 -5.0

default integer index: In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a 446934 5 1.0 2013-01-03 -0.918029 -1.032644 1.599718 5 2.0 2013-01-04 -1.236791 -0.438204 0.101452 5 3.0 2013-01-05 -1.632181 -0.992838 0.741029 5 4.0 2013-01-06 0.017195 -1.035754 0.960719 5 5.0 A where -1.0 2013-01-03 -0.918029 -1.032644 -1.599718 -5 -2.0 2013-01-04 -1.236791 -0.438204 -0.101452 -5 -3.0 2013-01-05 -1.632181 -0.992838 -0.741029 -5 -4.0 2013-01-06 -0.017195 -1.035754 -0.960719 -5 -5.0

the environment in which read_orc() can work. System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed Failed Access data in the cloud Dependency Minimum Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet 1.0 11.0 For more details and examples see the groupby documentation ) + [val]) for x, val in np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23

the environment in which read_orc() can work. System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed Failed Access data in the cloud Dependency Minimum Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet 1.0 11.0 For more details and examples see the groupby documentation ) + [val]) for x, val in np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23

the environment in which read_orc() can work. System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed Failed Access data in the cloud Dependency Minimum Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet 1.0 11.0 For more details and examples see the groupby documentation ) + [val]) for x, val in np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23

the environment in which read_orc() can work. System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed Failed Access data in the cloud Dependency Minimum Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet 1.0 11.0 For more details and examples see the groupby documentation ) + [val]) for x, val in np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23

the environment in which read_orc() can work. System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed Failed 1.4. Tutorials 11 pandas: powerful Python Out[11]: v1 v2 by1 by2 1 95 5.0 55.0 99 5.0 55.0 2 95 7.0 77.0 99 NaN NaN big damp 3.0 33.0 blue dry 3.0 33.0 red red 4.0 44.0 wet 1.0 11.0 For more details and examples see the groupby documentation ) + [val]) for x, val in np.ndenumerate(a)]) Out[29]: 0 1 2 3 0 0 0 0 1.0 1 0 0 1 2.0 2 0 0 2 3.0 3 0 0 3 4.0 4 0 1 0 5.0 .. .. .. .. ... 19 1 1 3 20.0 20 1 2 0 21.0 21 1 2 1 22.0 22 1 2 2 23