Github user steveloughran commented on a diff in the pull request: https://github.com/apache/spark/pull/12004#discussion_r89340299 --- Diff: docs/cloud-integration.md --- @@ -0,0 +1,953 @@ +--- +layout: global +displayTitle: Integration with Cloud Infrastructures +title: Integration with Cloud Infrastructures +description: Introduction to cloud storage support in Apache Spark SPARK_VERSION_SHORT +--- +<!--- + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. See accompanying LICENSE file. +--> + +* This will become a table of contents (this text will be scraped). +{:toc} + +## <a name="introduction"></a>Introduction + + +All the public cloud infrastructures, Amazon AWS, Microsoft Azure, Google GCS and others offer +persistent data storage systems, "object stores". These are not quite the same as classic file +systems: in order to scale to hundreds of Petabytes, without any single points of failure +or size limits, object stores, "blobstores", have a simpler model of `name => data`. + +Apache Spark can read or write data in object stores for data access. +through filesystem connectors implemented in Apache Hadoop or provided by third-parties. +These libraries make the object stores look *almost* like filesystems, with directories and +operations on files (rename) and directories (create, rename, delete) which mimic +those of a classic filesystem. Because of this, Spark and Spark-based applications +can work with object stores, generally treating them as as if they were slower-but-larger filesystems. + +With these connectors, Apache Spark supports object stores as the source +of data for analysis, including Spark Streaming and DataFrames. + + +## <a name="quick_start"></a>Quick Start + +Provided the relevant libraries are on the classpath, and Spark is configured with your credentials, +objects in an object store can be can be read or written through URLs which uses the name of the +object store client as the schema and the bucket/container as the hostname. + + +### Dependencies + +The Spark application neeeds the relevant Hadoop libraries, which can +be done by including the `spark-cloud` module for the specific version of spark used. + +The Spark application should include <code>hadoop-openstack</code> dependency, which can +be done by including the `spark-cloud` module for the specific version of spark used. +For example, for Maven support, add the following to the <code>pom.xml</code> file: + +{% highlight xml %} +<dependencyManagement> + ... + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-cloud_2.11</artifactId> + <version>${spark.version}</version> + </dependency> + ... +</dependencyManagement> +{% endhighlight %} + +If using the Scala 2.10-compatible version of Spark, the artifact is of course `spark-cloud_2.10`. + +### Basic Use + + + +To refer to a path in Amazon S3, use `s3a://` as the scheme (Hadoop 2.7+) or `s3n://` on older versions. + +{% highlight scala %} +sparkContext.textFile("s3a://landsat-pds/scene_list.gz").count() +{% endhighlight %} + +Similarly, an RDD can be saved to an object store via `saveAsTextFile()` + + +{% highlight scala %} +val numbers = sparkContext.parallelize(1 to 1000) + +// save to Amazon S3 (or compatible implementation) +numbers.saveAsTextFile("s3a://testbucket/counts") + +// Save to Azure Object store +numbers.saveAsTextFile("wasb://testbuc...@example.blob.core.windows.net/counts") + +// save to an OpenStack Swift implementation +numbers.saveAsTextFile("swift://testbucket.openstack1/counts") +{% endhighlight %} + +That's essentially it: object stores can act as a source and destination of data, using exactly +the same APIs to load and save data as one uses to work with data in HDFS or other filesystems. + +Because object stores are viewed by Spark as filesystems, object stores can +be used as the source or destination of any spark work âbe it batch, SQL, DataFrame, +Streaming or something else. + +The steps to do so are as follows + +1. Use the full URI to refer to a bucket, including the prefix for the client-side library +to use. Example: `s3a://landsat-pds/scene_list.gz` +1. Have the Spark context configured with the authentication details of the object store. +In a YARN cluster, this may also be done in the `core-site.xml` file. +1. Have the JAR containing the filesystem classes on the classpath âalong with all of its dependencies. + +### <a name="dataframes"></a>Example: DataFrames + +DataFrames can be created from and saved to object stores through the `read()` and `write()` methods. + +{% highlight scala %} +import org.apache.spark.SparkConf +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.types.StringType + +val spark = SparkSession + .builder + .appName("DataFrames") + .config(sparkConf) + .getOrCreate() +import spark.implicits._ +val numRows = 1000 + +// generate test data +val sourceData = spark.range(0, numRows).select($"id".as("l"), $"id".cast(StringType).as("s")) + +// define the destination +val dest = "wasb://yourcontai...@youraccount.blob.core.windows.net/dataframes" + +// write the data +val orcFile = dest + "/data.orc" +sourceData.write.format("orc").save(orcFile) + +// now read it back +val orcData = spark.read.format("orc").load(orcFile) + +// finally, write the data as Parquet +orcData.write.format("parquet").save(dest + "/data.parquet") +spark.stop() +{% endhighlight %} + +### <a name="streaming"></a>Example: Spark Streaming and Cloud Storage + +Spark Streaming can monitor files added to object stores, by +creating a `FileInputDStream` DStream monitoring a path under a bucket. + +{% highlight scala %} +import org.apache.spark.SparkConf +import org.apache.spark.sql.SparkSession +import org.apache.spark.streaming._ + +val sparkConf = new SparkConf() +val ssc = new StreamingContext(sparkConf, Milliseconds(5000)) +try { + val lines = ssc.textFileStream("s3a://bucket/incoming") + val matches = lines.filter(_.endsWith("3")) + matches.print() + ssc.start() + ssc.awaitTermination() +} finally { + ssc.stop(true) +} +{% endhighlight %} + +1. The time to scan for new files is proportional to the number of files +under the path ânot the number of *new* files, and that it can become a slow operation. +The size of the window needs to be set to handle this. + +1. Files only appear in an object store once they are completely written; there +is no need for a worklow of write-then-rename to ensure that files aren't picked up +while they are still being written. Applications can write straight to the monitored directory. + +#### <a name="checkpointing"></a>Checkpointing Streams to object stores + +Streams should only be checkpointed to an object store considered compatible with +HDFS. As the checkpoint operation includes a `rename()` operation, checkpointing to +an object store can be so slow that streaming throughput collapses. + + +## <a name="output"></a>Object Stores as a substitute for HDFS + +As the examples show, you can write data to object stores. However, that does not mean +That they can be used as replacements for a cluster-wide filesystem. + +The full details are covered in [Cloud Object Stores are Not Real Filesystems](#cloud_stores_are_not_filesystems). + +The brief summary is: + +| Object Store Connector | Replace HDFS? | +|-----------------------------|--------------------| +| Apache `s3a://` `s3n://` | No | +| Amazon EMR `s3://` | Yes | +| Microsoft Azure `wasb://` | Yes | +| OpenStack `swift://` | No | + +It is possible to use any of the object stores as a destination of work, i.e. use +`saveAsTextFile()` or `save` to save data there, but the commit process may be slow +and, unreliable in the presence of failures. + +It is faster and safer to use the cluster filesystem as the destination of Spark jobs, +using that data as the data for follow-on work. The final results can +be persisted in to the object store using `distcp`. + +### Recommended settings for writing to object stores + +Here are the settings to use when writing to object stores. This uses the "version 2" algorithm +for committing files âwhich does less renaming than the v1 algorithm. Speculative execution is +disabled to avoid multiple writers corrupting the output. + +``` +spark.speculation false +spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version 2 +spark.hadoop.mapreduce.fileoutputcommitter.cleanup-failures.ignored true +``` + +There's also the option of skipping the cleanup of temporary files in the output directory. +Enabling this option eliminates a small delay caused by listing and deleting any such files. + +``` +spark.hadoop.mapreduce.fileoutputcommitter.cleanup.skipped true +``` + +Bear in mind that storing temporary files can run up charges; Delete +directories called `"_temporary"` on a regular basis to avoid this. + + +### YARN Scheduler settings + +When running Spark in a YARN cluster running in EC2, turning off locality avoids any delays +waiting for the scheduler to find a node close to the data. + +```xml + <property> + <name>yarn.scheduler.capacity.node-locality-delay</name> + <value>0</value> + </property> +``` + +This has to be set in the YARN cluster configuration, not in the Spark configuration. + +### Parquet IO Settings + +For optimal performance when reading files saved in the Apache Parquet format, +read and write operations must be minimized, including generation of summary metadata, +and coalescing metadata from multiple files. The Predicate pushdown option +enables the Parquet library to skip un-needed columns, so saving bandwidth. + + spark.hadoop.parquet.enable.summary-metadata false + spark.sql.parquet.mergeSchema false + spark.sql.parquet.filterPushdown true + spark.sql.hive.metastorePartitionPruning true + +### ORC IO Settings + +For optimal performance when reading files saved in the Apache ORC format, +read and write operations must be minimized. Here are the options to achieve this. + + + spark.sql.orc.filterPushdown true + spark.sql.orc.splits.include.file.footer true + spark.sql.orc.cache.stripe.details.size 10000 + spark.sql.hive.metastorePartitionPruning true + +The Predicate pushdown option enables the ORC library to skip un-needed columns, and use index +information to filter out parts of the file where it can be determined that no columns match the predicate. + +The `spark.sql.orc.splits.include.file.footer` option means that the ORC file footer information, +is passed around with the file information âso eliminating the need to reread this data. + + +## <a name="authenticating"></a>Authenticating with Object Stores + +Apart from the special case of public read-only data, all object stores +require callers to authenticate themselves. +To do this, the Spark context must be configured with the authentication +details of the object store. + +1. In a YARN cluster, this may be done automatically in the `core-site.xml` file. +1. When Spark is running in cloud infrastructure (for example, on Amazon EC2, Google Cloud or +Microsoft Azure), the authentication details may be automatically derived from information +available to the VM. +1. `spark-submit` automatically picks up the contents of `AWS_ACCESS_KEY` and `AWS_SECRET_KEY` +environment variables and sets the associated configuration parameters for`s3n` and `s3a` +to these values. This essentially propagates the values across the Spark cluster. +1. Authentication details may be manually added to the Spark configuration +1. Alternatively, they can be programmatically added. *Important: never put authentication +secrets in source code. They will be compromised*. + +It is critical that the credentials used to access object stores are kept secret. Not only can +they be abused to run up compute charges, they can be used to read and alter private data. + +1. If adding login details to a spark configuration file, do not share this file, including +attaching to bug reports or committing it to SCM repositories. +1. Have different accounts for access to the storage for each application, +each with access rights restricted to those object storage buckets/containers used by the +application. +1. If the object store supports any form of session credential (e.g. Amazon's STS), issue +session credentials for the expected lifetime of the application. +1. When using a version of Spark with with Hadoop 2.8+ libraries, consider using Hadoop +credential files to store secrets, referencing +these files in the relevant ID/secret properties of the XML configuration file. + + +## <a name="object_stores"></a>Object stores and Their Library Dependencies + +The different object stores supported by Spark depend on specific Hadoop versions, +and require specific Hadoop JARs and dependent Java libraries on the classpath. + +<table class="table"> + <tr><th>Schema</th><th>Store</th><th>Details</th></tr> + <tr> + <td><code>s3a://</code></td> + <td>Amazon S3</a> + <td> + Recommended S3 client for Spark releases built on Apache Hadoop 2.7 or later. + </td> + </tr> + <tr> + <td><code>s3n://</code></td> + <td>Amazon S3</a> + <td> + Deprected S3 client; only use for Spark releases built on Apache Hadoop 2.6 or earlier. + </td> + </tr> + <tr> + <td><code>s3://</code></td> + <td>Amazon S3 on Amazon EMR</a> + <td> + Amazon's own S3 client; use only and exclusivley in Amazon EMR. + </td> + </tr> + <tr> + <td><code>wasb://</code></td> + <td>Azure Storage</a> + <td> + Client for Microsoft Azure Storage; since Hadoop 2.7. + </td> + </tr> + <tr> + <td><code>swift://</code></td> + <td>OpenStack Swift</a> + <td> + Client for OpenStack Swift object stores. + </td> + </tr> + <tr> + <td><code>gs://</code></td> + <td>Google Cloud Storage</a> + <td> + Google's client for their cloud object store. + </td> + </tr> +</table> + + +### <a name="working_with_amazon_s3"></a>Working with Amazon S3 + +Amazon's S3 object store is probably the most widely used object store âit is also the one +with the most client libraries. This is due to the evolution of Hadoop's support, and Amazon +offering Hadoop and Spark as its EMR service, along with its own S3 client. + +The recommendations for which client to use depend upon the version of Hadoop on the Spark classpath. + +<table class="table"> + <tr><th>Hadoop Library Version</th><th>Client</th></tr> + <tr> + <td>Hadoop 2.7+ and commercial products based on it</a> + <td><code>s3a://</code></td> + </tr> + <tr> + <td>Hadoop 2.6 or earlier</a> + <td><code>s3n://</code></td> + </tr> + <tr> + <td>Amazon EMR</a> + <td><code>s3://</code></td> + </tr> +</table> + +Authentication is generally via properties set in the spark context or, in YARN clusters, +`core-site.xml`. +Versions of the S3A client also support short-lived session credentials and IAM authentication to +automatically pick up credentials on EC2 deployments. Consult the appropriate Hadoop documentation for specifics. + +`spark-submit` will automatically pick up and propagate `AWS_ACCESS_KEY` and `AWS_SECRET_KEY` +from the environment variables set in the environment of the user running `spark-submit`; these +will override any set in the configuration files. + +Be aware that while S3 buckets support complex access control declarations, Spark needs +full read/write access to any bucket to which it must write data. That is: it does not support writing +to buckets where the root paths are read only, or not readable at all. + +#### <a name="s3a"></a>S3A Filesystem Client: `s3a://` + +The ["S3A" filesystem client](https://hadoop.apache.org/docs/stable2/hadoop-aws/tools/hadoop-aws/index.html) +shipped with in Hadoop 2.6, and has been considered ready for production use since Hadoop 2.7.1 + +*The S3A connector is the sole S3 connector undergoing active maintenance at the Apache, and +should be used wherever possible.* + +**Classpath** --- End diff -- I'll tighten it all down, and cut the CP details.
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