作者：eygle 发布在 eygle.com

如何更改一个定时任务的START_DATE / NEXT_RUN_DATE 时间？

有朋友修改服务器的时间后，所有定时任务停止运行，因为其运行时间处于未来，如下范例：

SQL> select job_name,state,enabled,start_date,next_run_date from DBA_SCHEDULER_JOBS;

JOB_NAME               STATE           ENABL START_DATE                   NEXT_RUN_DATE
------------------------------ --------------- ----- ---------------------------------------- ----------------------------------------
GATHER_STATS_JOB           DISABLED        FALSE 31-OCT-14 11.18.02.000000 PM +08:00      31-OCT-14 11.18.02.000000 PM +08:00

修复这个时间异常可以使用如下方法，先禁用任务，再通过SET_ATTRIBUTE的方式来修改：

SQL> exec dbms_scheduler.disable('gather_stats_job');

PL/SQL procedure successfully completed.

SQL> exec dbms_scheduler.set_attribute ('GATHER_STATS_JOB','start_date',systimestamp);

PL/SQL procedure successfully completed.

SQL>  select job_name,state,enabled,start_date,next_run_date from DBA_SCHEDULER_JOBS;

JOB_NAME               STATE           ENABL START_DATE                   NEXT_RUN_DATE
------------------------------ --------------- ----- ---------------------------------------- ----------------------------------------
GATHER_STATS_JOB           DISABLED        FALSE 04-FEB-12 11.19.30.550122 PM +08:00      31-OCT-14 11.18.02.000000 PM +08:00

SQL> exec dbms_scheduler.enable('gather_stats_job');

PL/SQL procedure successfully completed.

SQL> select job_name,state,enabled,start_date,next_run_date from DBA_SCHEDULER_JOBS;

JOB_NAME               STATE           ENABL START_DATE                   NEXT_RUN_DATE
------------------------------ --------------- ----- ---------------------------------------- ----------------------------------------
GATHER_STATS_JOB           RUNNING           TRUE  04-FEB-12 11.19.30.550122 PM +08:00      04-FEB-12 11.19.46.692031 PM +08:00

可以看到在启用SCHEDULER之后，NEXT_RUN_DATE会自动计算为正常值。

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Using Twitter Storm to analyze the Twitter Stream:

Francisco Jordano introduces briefly the concepts and provides some good resources for learnign about Twitter Storm just to present his experiment of using Twitter Storm for analyzing the Twitter (nb: the project is on GitHub ):

That’s how the information will flow, and the kind of tasks that we will execute. Yes it’s more effective to group some of those tasks, but remember, we just wanted to give this a try ;P

Worth emphasizing is that nowhere in the post is any reference of him having any troubles finding documentation or getting Twitter Storm up and running.

Original title and link: Using Twitter Storm to analyze the Twitter Stream (NoSQL database©myNoSQL)

Over the weekend I’ve read two papers presenting products or research related to improving or adding new capabilities to the MapReduce data processing approach. The first of them comes from a team at Microsoft and is describing TiMR a time-oriented data processing system in MapReduce. The second, from a team at Google, presents Tenzin - a SQL implementation on the MapReduce framework. It’s great to learn that while the Hadoop community is eliminating some of the initial limitations and hardening the technical details of the platform, there are already ideas and systems out there that augment the capabilities of the MapReduce data processing model.

Original title and link: Research in the MapReduce Space (NoSQL database©myNoSQL)

This recent paper from a team at Google is presenting details about Tenzing a system that is currently in use at Google:

Tenzing is a query engine built on top of MapReduce for ad hoc analysis of Google data. Tenzing supports a mostly complete SQL implementation (with several extensions) combined with several key characteristics such as heterogeneity, high performance, scalability, reliability, metadata awareness, low latency, support for columnar storage and structured data, and easy extensibility.

A couple of things I’ve highlighted when reading it:

• Tenzing is in production, but doesn’t serve yet a huge amount of queries
• the backend storage can be a mix of various data stores, such as ColumnIO, Bigtable, GFS files, MySQL databases
• when compared with other similar solutions (Sawzall, Flume-Java, Pig, Hive„ HadoopDB), Tenzing’s advantage is low latency
• the paper acknowledges AsterData, GreenPlum, Paraccel, Vertica for using a MapReduce execution model in their engines
• to perform query optimizations, Tenzing is enhancing queries with information from a metadata server
  • there is no information about what kind of metadata is needed in Tenzing. I assume it might refer to details about the data sources and data source metadata (indexes, access patterns, etc)
• to reduce query latency, processes are kept running
• Tenzing supports almost all SQL92 standard and some extensions from SQL99
  • projection and filtering (for some of these and depending on the data source Tenzing can do some optimizations)
  • set operations (implemented in the reduce phase)
  • nested queries and subqueries
  • aggregation and statistical functions
  • analytic functions (syntax similar to PostgreSQL/Oracle)
  • OLAP extensions

  • JOINs:

    Tenzing supports efficient joins across data sources, such as ColumnIO to Bigtable; inner, left, right, cross, and full outer joins; and equi semi-equi, non-equi and function based joins. Cross joins are only supported for tables small enough to fit in memory, and right outer joins are supported only with sort/merge joins. Non-equi correlated subqueries are currently not supported. We include distributed implementations for nested loop, sort/merge and hash joins.

Read and download the “Tenzing A SQL Implementation on the MapReduce framework” after the break.

Original title and link: Paper: Tenzing A SQL Implementation on the MapReduce Framework (NoSQL database©myNoSQL)

From the “Temporal Analytics on Big Data for Web Advertising” paper:

TiMR is a framework that transparently combines a map-reduce (M-R) system with a temporal DSMS¹. Users express time-oriented analytics using a temporal (DSMS) query lan- guage such as StreamSQL or LINQ. Streaming queries are declarative and easy to write/debug, real-time-ready, and often several orders of magnitude smaller than equivalent custom code for time-oriented applications. TiMR allows the temporal queries to transparently scale on offline temporal data in a cluster by leveraging existing M-R infrastructure.

Broadly speaking, TiMR’s architecture of compiling higher level queries into M-R stages is similar to that of Pig/SCOPE. However, TiMR specializes in time-oriented queries and data, with several new features such as: (1) the use of an unmodified DSMS as part of compilation, parallelization, and execution; and (2) the exploitation of new temporal parallelization opportunities unique to our setting. In addition, we leverage the temporal algebra underlying the DSMS in order to guarantee repeatability across runs in TiMR within M-R (when handling failures), as well as over live data.

According to the paper, DSMS work well for real-time data, but are not massively scalable. On the other hand, Map-Reduce is extremely scalable, but computation is performed on offline data. TiMR proposes a solution that is getting closer to a real-time map-reduce.

Read or download the paper after the break.

Original title and link: Paper: TiMR is a Time-oriented data processing system in MapReduce (NoSQL database©myNoSQL)

Ron Bodkin interviewed by Michael Floyd over InfoQ describes the Hadoop growing addiction:

People are using Hadoop for a variety of analytics. Many of the first uses of Hadoop are complementing traditional data warehouses I just mentioned, where the goal is to take some of the pressure of the data warehouse, start to be able to process less structured data more effectively and to be able to do transformations and build summaries and aggregates, but not have to have all that data loaded to the data warehouse. But then the next thing that happens is once people have started doing that level of processing they realize there is a power of being able to ask questions they never thought of before the data, they can store all the data in small samples and they can go back and have a powerful query engine, a cluster of commodity machines that lets them dig into that raw data and analyze it new ways ultimately leading to data science being able to do machine learning and being able to discover patterns in data and keep them improving and refining the data.

The interview is only 16 minutes long and you have the full transcript.

Original title and link: Hadoop and NoSQL in a Big Data Environment with Ron Bodkin (NoSQL database©myNoSQL)

To alternate a bit after yesterday’s educational CQL: SQL for Cassandra in the Cassandra NYC 2011 video series from DataStax, today’s video is Drew Robb covering Cassandra usage at SocialFlow for capturing real-time data from Twitter and Bit.ly.

For watching more videos from this event follow the Cassandra NYC 2011 tag.

Original title and link: Cassandra at SocialFlow with Drew Robb - Powered by NoSQL (NoSQL database©myNoSQL)