Return-Path: X-Original-To: archive-asf-public-internal@cust-asf2.ponee.io Delivered-To: archive-asf-public-internal@cust-asf2.ponee.io Received: from cust-asf.ponee.io (cust-asf.ponee.io [163.172.22.183]) by cust-asf2.ponee.io (Postfix) with ESMTP id 384C0200BF1 for ; Tue, 20 Dec 2016 02:33:53 +0100 (CET) Received: by cust-asf.ponee.io (Postfix) id 36FE8160B21; Tue, 20 Dec 2016 01:33:53 +0000 (UTC) Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by cust-asf.ponee.io (Postfix) with SMTP id DE3E2160B37 for ; Tue, 20 Dec 2016 02:33:51 +0100 (CET) Received: (qmail 28666 invoked by uid 500); 20 Dec 2016 01:33:51 -0000 Mailing-List: contact commits-help@carbondata.incubator.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@carbondata.incubator.apache.org Delivered-To: mailing list commits@carbondata.incubator.apache.org Received: (qmail 28657 invoked by uid 99); 20 Dec 2016 01:33:51 -0000 Received: from pnap-us-west-generic-nat.apache.org (HELO spamd2-us-west.apache.org) (209.188.14.142) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 20 Dec 2016 01:33:51 +0000 Received: from localhost (localhost [127.0.0.1]) by spamd2-us-west.apache.org (ASF Mail Server at spamd2-us-west.apache.org) with ESMTP id 6C8EF1A9A20 for ; Tue, 20 Dec 2016 01:33:50 +0000 (UTC) X-Virus-Scanned: Debian amavisd-new at spamd2-us-west.apache.org X-Spam-Flag: NO X-Spam-Score: -3.695 X-Spam-Level: X-Spam-Status: No, score=-3.695 tagged_above=-999 required=6.31 tests=[KAM_ASCII_DIVIDERS=0.8, KAM_LAZY_DOMAIN_SECURITY=1, KAM_LINEPADDING=1.2, RCVD_IN_DNSWL_HI=-5, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, RP_MATCHES_RCVD=-2.999, T_FILL_THIS_FORM_SHORT=0.01, URIBL_BLOCKED=0.001, URI_HEX=1.313] autolearn=disabled Received: from mx1-lw-eu.apache.org ([10.40.0.8]) by localhost (spamd2-us-west.apache.org [10.40.0.9]) (amavisd-new, port 10024) with ESMTP id C5PrrNZ9sLuc for ; Tue, 20 Dec 2016 01:33:46 +0000 (UTC) Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by mx1-lw-eu.apache.org (ASF Mail Server at mx1-lw-eu.apache.org) with SMTP id 6998660D17 for ; Tue, 20 Dec 2016 01:33:12 +0000 (UTC) Received: (qmail 26467 invoked by uid 99); 20 Dec 2016 01:33:11 -0000 Received: from git1-us-west.apache.org (HELO git1-us-west.apache.org) (140.211.11.23) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 20 Dec 2016 01:33:11 +0000 Received: by git1-us-west.apache.org (ASF Mail Server at git1-us-west.apache.org, from userid 33) id 80872DFD0E; Tue, 20 Dec 2016 01:33:11 +0000 (UTC) Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 8bit From: chenliang613@apache.org To: commits@carbondata.incubator.apache.org Date: Tue, 20 Dec 2016 01:33:44 -0000 Message-Id: <94d2405d7ec14b9f95bcf57afbde6bdd@git.apache.org> In-Reply-To: References: X-Mailer: ASF-Git Admin Mailer Subject: [35/53] [partial] incubator-carbondata-site git commit: updated website for release archived-at: Tue, 20 Dec 2016 01:33:53 -0000 http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/0bb92d4d/content/docs/quick_start.md ---------------------------------------------------------------------- diff --git a/content/docs/quick_start.md b/content/docs/quick_start.md deleted file mode 100644 index 5540e05..0000000 --- a/content/docs/quick_start.md +++ /dev/null @@ -1,140 +0,0 @@ - - -![CarbonData_Logo](../docs/images/format/CarbonData_logo.png?raw=true) -# Quick Start -This tutorial provides a quick introduction to using CarbonData. - -## Getting started with Apache CarbonData - -* [Installation](#installation) -* [Interactive Analysis with Carbon-Spark Shell](#InteractiveAnalysis-with-Carbon-Spark-Shell) - * [Basics](#basics) - * [Executing Queries](#executing-queries) - * [Prerequisites](#prerequisites) - * [Create Table](#create-table) - * [Load data to Table](#load-data-to-table) - * [Query data from table](#query-data-from-table) -* [Carbon SQL CLI](#carbon-sql-cli) - * [Basics](#basics) - * [Execute Queries in CLI](#execute-queries-in-cli) -* [Building CarbonData]() - - -## Installation -* Download released package of [Spark 1.5.0 to 1.6.2](http://spark.apache.org/downloads.html) -* Download and install [Apache Thrift 0.9.3](http://thrift-tutorial.readthedocs.io/en/latest/installation.html), make sure thrift is added to system path. -* Download [Apache CarbonData code](https://github.com/apache/incubator-carbondata) and build it. Please visit [Building CarbonData And IDE Configuration](Installing-CarbonData-And-IDE-Configuartion.md) for more information. - -## Interactive Analysis with Carbon-Spark Shell -Carbon Spark shell is a wrapper around Apache Spark Shell, it provides a simple way to learn the API, as well as a powerful tool to analyze data interactively. Please visit [Apache Spark Documentation](http://spark.apache.org/docs/latest/) for more details on Spark shell. - -#### Basics -Start Spark shell by running the following in the Carbon directory: -``` -./bin/carbon-spark-shell -``` -*Note*: In this shell SparkContext is readily available as sc and CarbonContext is available as cc. - -CarbonData stores and writes the data in its specified format at the default location on the hdfs. -By default carbon.storelocation is set as : -``` -hdfs://IP:PORT/Opt/CarbonStore -``` -And you can provide your own store location by providing configuration using --conf option like: -``` -./bin/carbon-spark-sql --conf spark.carbon.storepath= -``` - -#### Executing Queries - -**Prerequisites** - -Create sample.csv file in CarbonData directory. The CSV is required for loading data into Carbon. -``` -$ cd carbondata -$ cat > sample.csv << EOF - id,name,city,age - 1,david,shenzhen,31 - 2,eason,shenzhen,27 - 3,jarry,wuhan,35 - EOF -``` - -**Create table** - -``` -scala>cc.sql("create table if not exists test_table (id string, name string, city string, age Int) STORED BY 'carbondata'") -``` - -**Load data to table** -``` -scala>val dataFilePath = new File("../carbondata/sample.csv").getCanonicalPath -scala>cc.sql(s"load data inpath '$dataFilePath' into table test_table") -``` - -**Query data from table** - -``` -scala>cc.sql("select * from test_table").show -scala>cc.sql("select city, avg(age), sum(age) from test_table group by city").show -``` - -## Carbon SQL CLI -The Carbon Spark SQL CLI is a wrapper around Apache Spark SQL CLI. It is a convenient tool to execute queries input from the command line. Please visit [Apache Spark Documentation](http://spark.apache.org/docs/latest/) for more information Spark SQL CLI. - -#### Basics -Start the Carbon Spark SQL CLI, run the following in the Carbon directory : - -``` -./bin/carbon-spark-sql -``` -CarbonData stores and writes the data in its specified format at the default location on the hdfs. -By default carbon.storelocation is set as : -``` -hdfs://IP:PORT/Opt/CarbonStore -``` - -And you can provide your own store location by providing configuration using --conf option like: -``` -./bin/carbon-spark-sql --conf spark.carbon.storepath=/home/root/carbonstore -``` - -#### Execute Queries in CLI -``` -spark-sql> create table if not exists test_table (id string, name string, city string, age Int) STORED BY 'carbondata' -spark-sql> load data inpath '../sample.csv' into table test_table -spark-sql> select city, avg(age), sum(age) from test_table group by city -``` -## Building CarbonData - -To get started, get CarbonData from the [downloads]() on the [http://carbondata.incubator.apache.org.](http://carbondata.incubator.apache.org.) -CarbonData uses Hadoop’s client libraries for HDFS and YARN and Spark's libraries. Downloads are pre-packaged for a handful of popular Spark versions. - -If you’d like to build CarbonData from source, Please visit [Building CarbonData And IDE Configuration]() - - - - - - - - - - http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/0bb92d4d/content/docs/troubleshooting.md ---------------------------------------------------------------------- diff --git a/content/docs/troubleshooting.md b/content/docs/troubleshooting.md deleted file mode 100644 index d13de95..0000000 --- a/content/docs/troubleshooting.md +++ /dev/null @@ -1,32 +0,0 @@ - - -# Troubleshooting -This tutorial is designed to provide troubleshooting for end users and developers -who are building, deploying, and using CarbonData. - -* ## Prerequisites for Developers - -* ## Prerequisites for End Users - -* ## General Prevention and Best Practices - -* ## Procedure(s) - -* ## References http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/0bb92d4d/content/docs/use_cases.md ---------------------------------------------------------------------- diff --git a/content/docs/use_cases.md b/content/docs/use_cases.md deleted file mode 100644 index 87c2381..0000000 --- a/content/docs/use_cases.md +++ /dev/null @@ -1,82 +0,0 @@ - - -# CarbonData Use Cases -This tutorial will discuss about the problems that CarbonData address.It shall take you through the identified top use cases of Carbon. - -## Introduction -For big data interactive analysis scenarios, many customers expect sub-second response to query TB-PB level data on general hardware clusters with just a few nodes. - -In the current big data ecosystem, there are few columnar storage formats such as ORC and Parquet that are designed for SQL on Big Data. Apache Hive’s ORC format is -a columnar storage format with basic indexing capability. However, ORC cannot meet the sub-second query response expectation on TB level data, because ORC format -performs only stride level dictionary encoding and all analytical operations such as filtering and aggregation is done on the actual data. Apache Parquet is columnar -storage can improve performance in comparison to ORC, because of more efficient storage organization. Though Parquet can provide query response on TB level data in a -few seconds, it is still far from the sub-second expectation of interactive analysis users. Cloudera Kudu can effectively solve some query performance issues, but kudu -is not hadoop native, can’t seamlessly integrate historic HDFS data into new kudu system. - -However, CarbonData uses specially engineered optimizations targeted to improve performance of analytical queries which can include filters, aggregation and distinct counts, -the required data to be stored in an indexed, well organized, read-optimized format, CarbonData’s query performance can achieve sub-second response. - -## Motivation: Single Format to provide low latency response for all use cases -The main motivation behind CarbonData is to provide a single storage format for all the usecases of querying big data on Hadoop. Thus CarbonData is able to cover all use-cases -into a single storage format. - - ![Motivation](../docs/images/format/carbon_data_motivation.png?raw=true) - -## Use Cases -* ### Sequential Access - - Supports queries that select only a few columns with a group by clause but do not contain any filters. - This results in full scan over the complete store for the selected columns. - - ![Sequential_Scan](../docs/images/format/carbon_data_full_scan.png?raw=true) - - **Scenario** - - - ETL jobs - - Log Analysis - -* ### Random Access - - Supports Point Query. These are queries used from operational applications and usually select all or most of the columns but do involve a large number of - filters which reduce the result to a small size. Such queries generally do not involve any aggregation or group by clause. - - Row-key query(like HBase) - - Narrow Scan - - Requires second/sub-second level low latency - - ![random_access](../docs/images/format/carbon_data_random_scan.png?raw=true) - - **Scenario** - - - Operational Query - - User Profiling - -* ### Olap Style Query - - Supports Interactive data analysis for any dimensions. These are queries which are typically fired from Interactive Analysis tools. - Such queries often select a few columns but involve filters and group by on a column or a grouping expression. - It also supports queries that : - - involves aggregation/join - - Roll-up,Drill-down,Slicing and Dicing - - Low-latency ad-hoc query - - ![Olap_style_query](../docs/images/format/carbon_data_olap_scan.png?raw=true) - - **Scenario** - - - Dash-board reporting - - Fraud & Ad-hoc Analysis - http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/0bb92d4d/content/docs/user_guide.md ---------------------------------------------------------------------- diff --git a/content/docs/user_guide.md b/content/docs/user_guide.md deleted file mode 100644 index 6db4534..0000000 --- a/content/docs/user_guide.md +++ /dev/null @@ -1,47 +0,0 @@ - -![CarbonData_Logo](../docs/images/format/CarbonData_logo.png?raw=true) -# User Guide -Welcome to Apache CarbonData. Apache CarbonData(incubating) is a new big data file format for faster interactive query using advanced columnar storage, index, compression and encoding techniques to improve computing efficiency, in turn it will help speedup queries an order of magnitude faster over PetaBytes of data. -This user guide provides a detailed description about the CarbonData and its features. - -Let's get started ! - -* [Overview]() - * [Introduction]() - * [Features]() - * [Data Types]() - * [Compatibility]() - * [Packaging and Interfaces]() -* [Installing CarbonData]() - * [Standalone Spark Cluster]() - * [Spark on Yarn Cluster]() -* [Configuring CarbonData]() - * [System Configuration]() - * [Performance Configuration]() - * [Spark Configuration]() -* [Using CarbonData]() - * [Data Management]() - * [DDL Operations]() - * [DML Operations]() - - - - - http://git-wip-us.apache.org/repos/asf/incubator-carbondata-site/blob/0bb92d4d/content/faqDoc.html ---------------------------------------------------------------------- diff --git a/content/faqDoc.html b/content/faqDoc.html new file mode 100644 index 0000000..471e9f0 --- /dev/null +++ b/content/faqDoc.html @@ -0,0 +1,115 @@ + + + + + + + + CarbonData + + + + + + + + + + +
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