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author | Matei Alexandru Zaharia <matei@apache.org> | 2015-07-25 23:10:48 +0000 |
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committer | Matei Alexandru Zaharia <matei@apache.org> | 2015-07-25 23:10:48 +0000 |
commit | f4fb827ef5aa831ace6f0ce21d6b02e83f409b63 (patch) | |
tree | 9c84b511d584f0f9cd3500f6a887fc92d8348955 /site/sql | |
parent | 2de4e60511dad1ec7e4ac3974b14dcf85faaad50 (diff) | |
download | spark-website-f4fb827ef5aa831ace6f0ce21d6b02e83f409b63.tar.gz spark-website-f4fb827ef5aa831ace6f0ce21d6b02e83f409b63.tar.bz2 spark-website-f4fb827ef5aa831ace6f0ce21d6b02e83f409b63.zip |
Updates to SQL page
Diffstat (limited to 'site/sql')
-rw-r--r-- | site/sql/index.html | 33 |
1 files changed, 15 insertions, 18 deletions
diff --git a/site/sql/index.html b/site/sql/index.html index e14fc7171..1f1b6ab0c 100644 --- a/site/sql/index.html +++ b/site/sql/index.html @@ -6,7 +6,7 @@ <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> - Spark SQL | Apache Spark + Spark SQL & DataFrames | Apache Spark </title> @@ -93,7 +93,7 @@ Libraries <b class="caret"></b> </a> <ul class="dropdown-menu"> - <li><a href="/sql/">Spark SQL</a></li> + <li><a href="/sql/">SQL and DataFrames</a></li> <li><a href="/streaming/">Spark Streaming</a></li> <li><a href="/mllib/">MLlib (machine learning)</a></li> <li><a href="/graphx/">GraphX (graph)</a></li> @@ -160,7 +160,7 @@ Built-in Libraries: </p> <ul class="list-none"> - <li><a href="/sql/">Spark SQL</a></li> + <li><a href="/sql/">SQL and DataFrames</a></li> <li><a href="/streaming/">Spark Streaming</a></li> <li><a href="/mllib/">MLlib (machine learning)</a></li> <li><a href="/graphx/">GraphX (graph)</a></li> @@ -181,8 +181,7 @@ Seamlessly mix SQL queries with Spark programs. </p> <p> - Spark SQL lets you query structured data as a distributed dataset (RDD) in Spark, with integrated APIs in Python, Scala and Java. - This tight integration makes it easy to run SQL queries alongside complex analytic algorithms. + Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar <a href="/docs/latest/sql-programming-guide.html">DataFrame API</a>. Usable in Java, Scala, Python and R. </p> </div> <div class="col-md-5 col-sm-5 col-padded-top col-center"> @@ -200,19 +199,19 @@ <div class="row row-padded"> <div class="col-md-7 col-sm-7"> - <h2>Unified Data Access</h2> + <h2>Uniform Data Access</h2> <p class="lead"> - Load and query data from a variety of sources. + Connect to any data source the same way. </p> <p> - SchemaRDDs provide a single interface for efficiently working with structured data, including Apache Hive tables, parquet files and JSON files. + DataFrames and SQL provide a common way to access a variety of data sources, including Hive, Avro, Parquet, ORC, JSON, and JDBC. You can even join data across these sources. </p> </div> <div class="col-md-5 col-sm-5 col-padded-top col-center"> <div style="margin-top: 15px; text-align: left; display: inline-block;"> <div class="code"> sqlCtx.<span class="sparkop">jsonFile</span>(<span class="closure">"s3n://..."</span>)<br /> .registerAsTable("json")<br /> - schema_rdd = sqlCtx.<span class="sparkop">sql</span>(<span class="closure">"""<br /> + results = sqlCtx.<span class="sparkop">sql</span>(<span class="closure">"""<br /> SELECT * <br /> FROM hiveTable<br /> JOIN json ..."""</span>)<br /> @@ -226,7 +225,7 @@ <div class="col-md-7 col-sm-7"> <h2>Hive Compatibility</h2> <p class="lead"> - Run unmodified Hive queries on existing warehouses. + Run unmodified Hive queries on existing data. </p> <p> Spark SQL reuses the Hive frontend and metastore, giving you full compatibility with @@ -248,7 +247,7 @@ Connect through JDBC or ODBC. </p> <p> - Spark SQL includes a server mode with industry standard JDBC and ODBC connectivity. + A server mode provides industry standard JDBC and ODBC connectivity for business intelligence tools. </p> </div> <div class="col-md-5 col-sm-5 col-padded-top col-center"> @@ -288,13 +287,11 @@ <div class="row"> <div class="col-md-4 col-padded"> - <h3>Scalability</h3> + <h3>Performance & Scalability</h3> <p> - Use the same engine for both interactive and long queries. - </p> - <p> - Spark SQL takes advantage of the RDD model to support mid-query fault tolerance, letting it scale to large jobs too. - Don't worry about using a different engine for historical data. + Spark SQL includes a cost-based optimizer, columnar storage and code generation to make queries fast. + At the same time, it scales to thousands of nodes and multi hour queries using the Spark engine, which provides full mid-query fault tolerance. + Don't worry about using a different engine for historical data. </p> </div> @@ -322,7 +319,7 @@ </p> <ul class="list-narrow"> <li><a href="/downloads.html">Download Spark</a>. It includes Spark SQL as a module.</li> - <li>Read the <a href="/docs/latest/sql-programming-guide.html">Spark SQL programming guide</a>, which includes a examples of common use cases.</li> + <li>Read the <a href="/docs/latest/sql-programming-guide.html">Spark SQL and DataFrame guide</a> to learn the API.</li> </ul> </div> </div> |