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

repository/org/apache/servicemix/bundles/ org.apache.servicemix.bundles.cglib/3.0_1/org.apache.servicemix.bundles.cglib-3.0_1.jar 35859 2013-12-06 10:52 repository/org/springframework/osgi/spring-osgi-io/1 │ true │ false │ 1 zabbix/zabbix-java-gateway │ Zabbix Java Gateway │ true │ false │ 13 jetty It’s not always easy to use a JMX client with the RMI protocol. Some monitoring tools (Nagios, Zabbix, …) are not native JMX clients. But most of them can use HTTP. More over, you may want to write

__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