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Measuring Time and Progress in Streaming Pipelines. 但何为low watermark，为什么需要它与其他方式相比有什么优劣实时计算已出现多年，为什么此时才需要解决时序性 Paper里最关键的地方是什么本文尝试解答这些问题。 In the case of Zeitgeist, shown in Figure 1, our input would be a continuously arriving set of search queries, and our output would be the set of queries that are spiking or dipping. It is important to be able to distinguish whether a flurry of expected Arabic queries at t = 1296167641 is simply delayed on the wire, or actually not there. Output Low Watermark: Oldes...

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flykobe的技术与生活杂谈 | flykobe.com Reviews

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Measuring Time and Progress in Streaming Pipelines. 但何为low watermark，为什么需要它与其他方式相比有什么优劣实时计算已出现多年，为什么此时才需要解决时序性 Paper里最关键的地方是什么本文尝试解答这些问题。 In the case of Zeitgeist, shown in Figure 1, our input would be a continuously arriving set of search queries, and our output would be the set of queries that are spiking or dipping. It is important to be able to distinguish whether a flurry of expected Arabic queries at t = 1296167641 is simply delayed on the wire, or actually not there. Output Low Watermark: Oldes...

<META> KEYWORDS

1 flykobe的技术与生活杂谈

2 google low watermark学习笔记

3 时序性问题

4 从多个机房/地域采集数据，导致数据乱序局部有序、整体乱序

5 某机器或进程故障，重启后数据大大延迟

6 某机房或交换机故障，恢复后数据大大延迟

7 波峰时由于数据量大，少量数据延迟影响可以忽略

8 low watermark原理

9 definition

10 对于复杂问题，我们往往将其拆分为子问题，这里也一样，将其转化为递归

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flykobe的技术与生活杂谈,google low watermark学习笔记,时序性问题,从多个机房/地域采集数据，导致数据乱序局部有序、整体乱序,某机器或进程故障，重启后数据大大延迟,某机房或交换机故障，恢复后数据大大延迟,波峰时由于数据量大，少量数据延迟影响可以忽略,low watermark原理,definition,对于复杂问题，我们往往将其拆分为子问题，这里也一样，将其转化为递归,进一步又区分了input和output low watermark,这里有两个重要的隐藏结论,关键技术点

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flykobe的技术与生活杂谈 | flykobe.com Reviews

https://flykobe.com

Measuring Time and Progress in Streaming Pipelines. 但何为low watermark，为什么需要它与其他方式相比有什么优劣实时计算已出现多年，为什么此时才需要解决时序性 Paper里最关键的地方是什么本文尝试解答这些问题。 In the case of Zeitgeist, shown in Figure 1, our input would be a continuously arriving set of search queries, and our output would be the set of queries that are spiking or dipping. It is important to be able to distinguish whether a flurry of expected Arabic queries at t = 1296167641 is simply delayed on the wire, or actually not there. Output Low Watermark: Oldes...

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flykobe的技术与生活杂谈 » Blog Archive » SparkSQL与Parquet数据结构的交互过程

http://flykobe.com/index.php/2015/07/22/sparksql-parquet

Laquo; [zz]You Do It Too: Forfeiting Partition Tolerance in Distributed Systems. Zz]How-to: Tune Your Apache Spark Jobs. 但是使用tags map string, array 存储标签数据，还是tag xxx array的格式呢两者是否都能使用到spark的pushdown功能，以跳过不需要的columns呢. Http:/ www.csdn.net/article/2014-07-15/2820658/2. Http:/ blog.csdn.net/oopsoom/article/details/37658021. Http:/ mmicky.blog.163.com/blog/static/150290154201487102349800/ 共十篇，比较全面. Core 处理数据的输入输出，从不同的数据源获取数据 RDD、Parquet、json等，将查询结果输出成schemaRDD. Path, logicalPlan ). Path, child ). The conver...

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flykobe的技术与生活杂谈 » Blog Archive » [zz]盘点SQL on Hadoop中用到的主要技术

http://flykobe.com/index.php/2015/07/14/zz盘点sql-on-hadoop中用到的主要技术

Laquo; [zz] What Kind of Hive Table is Best for Your Data? Zz]Impala 表使用 Parquet 文件格式. Zz from: http:/ sunyi514.github.io/2014/11/15/%E7%9B%98%E7%82%B9sql-on-hadoop%E4%B8%AD%E7%94%A8%E5%88%B0%E7%9A%84%E4%B8%BB%E8%A6%81%E6%8A%80%E6%9C%AF/. Runtime framework v.s. mpp. 在SQL on Hadoop系统中，有两种架构，一种是基于某个运行时框架来构建查询引擎，典型案例是Hive 另一种是仿照过去关系数据库的MPP架构。一般来说，对于SQL on Hadoop系统很重要的一个评价指标就是快。这时候无论是有实力的大公司比如Facebook ，还是专业的供应商比如Cloudera ，都试图去解决这个问题。 DAG vs. MR 最主要的优势，中间结果不写磁盘除非内存不够，一气呵成。 C1id = c2.id where. 时发现，Hive在...

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flykobe的技术与生活杂谈 » Blog Archive » [zz]Impala 表使用 Parquet 文件格式

http://flykobe.com/index.php/2015/07/14/zzimpala-表使用-parquet-文件格式

Laquo; [zz]盘点SQL on Hadoop中用到的主要技术. Zz]You Do It Too: Forfeiting Partition Tolerance in Distributed Systems. Zz]Impala 表使用 Parquet 文件格式. Zz from: http:/ www.cloudera.com/content/cloudera/zh-CN/documentation/core/v5-3-x/topics/impala parquet.html. Impala 可帮助您创建、管理和查询 Parquet 表。 Parquet 是一种面向列的二进制文件格式，其设计目标是为 Impala 最擅长的大规模查询类型提供高效率支持。针对 Parquet 表的查询可以快速并以最小的 I/O 从任意列快速获取并分析这些数据。 1 Impala 支持 Parquet 文件. 在 Impala 中创建 Parquet 表. Parquet 数据文件的 Snappy 和 GZip 压缩. 与其他 Hadoop 组件交换 Parquet 数据文件. STORED AS PARQUET;.

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flykobe的技术与生活杂谈

http://flykobe.com/index.php/page/2

提到parquet.block.size所控制的parquet row group大小是一个需要调优的spark参数。以下将对比spark 1.4和我厂根据1.5打的patch版本，分析两者的实现方式和优劣。该版本的SqlNewHadoopRDD.getPartitions()里，使用FilteringParquetRowInputFormat类，在spark.sql.parquet.cacheMetadata=true 默认时，会使用cachedStatus和cachedFooters缓存，覆盖父类的listStatus()和getFooters()方法。 Overridden so we can inject our own cached files statuses. Id, i, rawSplits (. 但可以通过设置parquet.task.side.metadata=false，使以rowGroup为单位调度当group太小时，会合并，保证满足minsize。 Static FileMetaData filterFileMetaData (. Comment on this post.

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flykobe的技术与生活杂谈 » spark

http://flykobe.com/index.php/category/spark

Archive for the ‘spark’ Category. Laquo; Previous Entries. 本文尝试用spark自带example streaming.NetworkWordCount为例，解释spark streaming 1.6.0的执行流程。 Create the context with a 1 second batch size. SparkConf, Minutes (. Create a socket stream on target ip:port and count the. Words in input stream of n delimited text (eg. generated by 'nc'). Note that no duplication in storage level only for running locally. Replication necessary in distributed scenario for fault tolerance. StorageLevel. MEMORY AND DISK SER. BlockGene...

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workshop.rs

mooBarGraph: AJAX graph for MooTools – workshop.rs – owned by ivan lazarevic

http://workshop.rs/2010/02/moobargraph-ajax-graph-for-mootools

Owned by Lazarevic Ivan. MooBarGraph: AJAX graph for MooTools. MooBarGraph is AJAX graph plugin for MooTools. Which support two types of graphs, simple bar and stacked bar graph. This plugin made graph from your JSON data and is compatible with all major browsers including IE6. On steroids. It have almost all jqBarGraph possibility, plus some new features, like url bars, info boxes, negative values, AJAX data loading…. See how it works. Try it for yourself. Bars can be simple or stacked. After you create...

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flykobe的技术与生活杂谈

Measuring Time and Progress in Streaming Pipelines. 但何为low watermark，为什么需要它与其他方式相比有什么优劣实时计算已出现多年，为什么此时才需要解决时序性 Paper里最关键的地方是什么本文尝试解答这些问题。 In the case of Zeitgeist, shown in Figure 1, our input would be a continuously arriving set of search queries, and our output would be the set of queries that are spiking or dipping. It is important to be able to distinguish whether a flurry of expected Arabic queries at t = 1296167641 is simply delayed on the wire, or actually not there. Output Low Watermark: Oldes...

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