From 59f9fafd6bcecb3d9edfbf35a33c746b6f904e39 Mon Sep 17 00:00:00 2001 From: rickyhuo Date: Tue, 9 Apr 2019 22:25:00 +0800 Subject: [PATCH 1/4] Update docs for structured streaming --- README.md | 2 ++ docs/images/wd-workflow.png | Bin 84510 -> 20565 bytes docs/zh-cn/quick-start.md | 10 ++++++++++ 3 files changed, 12 insertions(+) diff --git a/README.md b/README.md index 80a62cf084c..906b30fb5bc 100644 --- a/README.md +++ b/README.md @@ -27,6 +27,7 @@ Waterdrop 提供可直接执行的软件包,没有必要自行编译源代码 Databricks 开源的 Apache Spark 对于分布式数据处理来说是一个伟大的进步。我们在使用 Spark 时发现了很多可圈可点之处,同时我们也发现了我们的机会 —— 通过我们的努力让Spark的使用更简单,更高效,并将业界和我们使用Spark的优质经验固化到Waterdrop这个产品中,明显减少学习成本,加快分布式数据处理能力在生产环境落地。 除了大大简化分布式数据处理难度外,Waterdrop尽所能为您解决可能遇到的问题: + * 数据丢失与重复 * 任务堆积与延迟 * 吞吐量低 @@ -51,6 +52,7 @@ Databricks 开源的 Apache Spark 对于分布式数据处理来说是一个伟 * 海量数据处理能力 * 模块化和插件化,易于扩展 * 支持利用SQL做数据处理和聚合 +* 支持Spark Structured Streaming * 支持Spark 2.x ## Waterdrop 的工作流程 diff --git a/docs/images/wd-workflow.png b/docs/images/wd-workflow.png index 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z=-OL%Z}uVs;)^QR&#B$?WlLM4D1zxnoOeuEcgs0qjovRgN+5tJ6&|oA_sC2`Y$ins zEjC^x(T@}-pFmNAJ6g}TC+ci;EcY>BJVArDrJEh(9g3zd^BSDYqR-xDJ11&>a+y%@7Pw5i zXzl`4Pz}Ke%*JXAGu0F(s51fQD?UdQ-3`0z#`g5Q@Z%L5fs-kRGMsyWw+gq(39S*@ z^kci*#3#VC160d#%jT?Fa-%~tX9m00_Rw%WUdQ9W>rJjdn73?o{Tzds6@a diff --git a/docs/zh-cn/quick-start.md b/docs/zh-cn/quick-start.md index c3e01096139..a242ea0398b 100644 --- a/docs/zh-cn/quick-start.md +++ b/docs/zh-cn/quick-start.md @@ -118,3 +118,13 @@ cd waterdrop ./bin/start-waterdrop.sh --master local[4] --deploy-mode client --config ./config/batch.conf.template ``` + +[配置示例3 : Structured Streaming 流式处理](https://github.com/InterestingLab/waterdrop/blob/master/config/structuredstreaming.conf.template) + +以上配置为默认【Structured Streaming 配置模版】,需配置Kafka输入源后运行,命令如下: + +``` +cd waterdrop +./bin/start-waterdrop-structured-streaming.sh --master local[4] --deploy-mode client --config ./config/batch.conf.template + +``` \ No newline at end of file From 303e63f12a9c18ed1284756d226821bd24df25ff Mon Sep 17 00:00:00 2001 From: rickyhuo Date: Tue, 9 Apr 2019 22:29:33 +0800 Subject: [PATCH 2/4] Update docs/zh-cn/README.md --- docs/zh-cn/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/zh-cn/README.md b/docs/zh-cn/README.md index d4b60e9ef73..bace4c7c544 100644 --- a/docs/zh-cn/README.md +++ b/docs/zh-cn/README.md @@ -48,6 +48,7 @@ Databricks 开源的 Apache Spark 对于分布式数据处理来说是一个伟 * 海量数据处理能力 * 模块化和插件化,易于扩展 * 支持利用SQL做数据处理和聚合 +* Spark Structured Streaming * 支持Spark 2.x ## Waterdrop 的工作流程 From 19e574769ec8765ed5798134ee5d2db4283fb6a6 Mon Sep 17 00:00:00 2001 From: rickyhuo Date: Wed, 10 Apr 2019 09:29:54 +0800 Subject: [PATCH 3/4] Fix markdown code style --- README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 906b30fb5bc..eaa48547d25 100644 --- a/README.md +++ b/README.md @@ -45,15 +45,15 @@ Databricks 开源的 Apache Spark 对于分布式数据处理来说是一个伟 ## Waterdrop 的特性 -* 简单易用,灵活配置,无需开发 -* 实时流式处理 -* 离线多源数据分析 -* 高性能 -* 海量数据处理能力 -* 模块化和插件化,易于扩展 -* 支持利用SQL做数据处理和聚合 -* 支持Spark Structured Streaming -* 支持Spark 2.x +* 简单易用,灵活配置,无需开发 +* 实时流式处理 +* 离线多源数据分析 +* 高性能 +* 海量数据处理能力 +* 模块化和插件化,易于扩展 +* 支持利用SQL做数据处理和聚合 +* 支持Spark Structured Streaming +* 支持Spark 2.x ## Waterdrop 的工作流程 From fc49aa465837bb3824a6e50dc23bb1e11ec8bb4c Mon Sep 17 00:00:00 2001 From: rickyhuo Date: Wed, 10 Apr 2019 09:37:03 +0800 Subject: [PATCH 4/4] Fix markdown code style --- README.md | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index eaa48547d25..a07bb554897 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,6 @@ Databricks 开源的 Apache Spark 对于分布式数据处理来说是一个伟 * 应用到生产环境周期长 * 缺少应用运行状态监控 - "Waterdrop" 的中文是“水滴”,来自中国当代科幻小说作家刘慈欣的《三体》系列,它是三体人制造的宇宙探测器,会反射几乎全部的电磁波,表面绝对光滑,温度处于绝对零度,全部由被强互作用力紧密锁死的质子与中子构成,无坚不摧。在末日之战中,仅一个水滴就摧毁了人类太空武装力量近2千艘战舰。 ## Waterdrop 使用场景 @@ -45,15 +44,15 @@ Databricks 开源的 Apache Spark 对于分布式数据处理来说是一个伟 ## Waterdrop 的特性 -* 简单易用,灵活配置,无需开发 -* 实时流式处理 -* 离线多源数据分析 -* 高性能 -* 海量数据处理能力 -* 模块化和插件化,易于扩展 -* 支持利用SQL做数据处理和聚合 -* 支持Spark Structured Streaming -* 支持Spark 2.x +* 简单易用,灵活配置,无需开发 +* 实时流式处理 +* 离线多源数据分析 +* 高性能 +* 海量数据处理能力 +* 模块化和插件化,易于扩展 +* 支持利用SQL做数据处理和聚合 +* 支持Spark Structured Streaming +* 支持Spark 2.x ## Waterdrop 的工作流程