[FLINK-12378][docs] Consolidate FileSystem Documentation

docs/ops/deployment/aws.md

@@ -64,81 +64,11 @@ HADOOP_CONF_DIR=/etc/hadoop/conf ./bin/flink run -m yarn-cluster -yn 1 examples/
 
 {% top %}
 
-## S3: Simple Storage Service
-
-[Amazon Simple Storage Service](https://aws.amazon.com/s3/) (Amazon S3) provides cloud object storage for a variety of use cases. You can use S3 with Flink for **reading** and **writing data** as well in conjunction with the [streaming **state backends**]({{ site.baseurl}}/ops/state/state_backends.html) or even as a YARN object storage.
-
-You can use S3 objects like regular files by specifying paths in the following format:
-
-{% highlight plain %}
-s3:https://<your-bucket>/<endpoint>
-{% endhighlight %}
-
-The endpoint can either be a single file or a directory, for example:
-
-{% highlight java %}
-// Read from S3 bucket
-env.readTextFile("s3:https://<bucket>/<endpoint>");
-
-// Write to S3 bucket
-stream.writeAsText("s3:https://<bucket>/<endpoint>");
-
-// Use S3 as FsStatebackend
-env.setStateBackend(new FsStateBackend("s3:https://<your-bucket>/<endpoint>"));
-{% endhighlight %}
-
-Note that these examples are *not* exhaustive and you can use S3 in other places as well, including your [high availability setup](../jobmanager_high_availability.html) or the [RocksDBStateBackend]({{ site.baseurl }}/ops/state/state_backends.html#the-rocksdbstatebackend); everywhere that Flink expects a FileSystem URI.
-
-For most use cases, you may use one of our shaded `flink-s3-fs-hadoop` and `flink-s3-fs-presto` S3
-filesystem wrappers which are fairly easy to set up. For some cases, however, e.g. for using S3 as
-YARN's resource storage dir, it may be necessary to set up a specific Hadoop S3 FileSystem
-implementation. Both ways are described below.
-
-### Shaded Hadoop/Presto S3 file systems (recommended)
-
-{% panel **Note:** You don't have to configure this manually if you are running [Flink on EMR](#emr-elastic-mapreduce). %}
-
-To use either `flink-s3-fs-hadoop` or `flink-s3-fs-presto`, copy the respective JAR file from the
-`opt` directory to the `lib` directory of your Flink distribution before starting Flink, e.g.
-
-{% highlight bash %}
-cp ./opt/flink-s3-fs-presto-{{ site.version }}.jar ./lib/
-{% endhighlight %}
-
-Both `flink-s3-fs-hadoop` and `flink-s3-fs-presto` register default FileSystem
-wrappers for URIs with the `s3:https://` scheme, `flink-s3-fs-hadoop` also registers
-for `s3a:https://` and `flink-s3-fs-presto` also registers for `s3p:https://`, so you can
-use this to use both at the same time.
-
-#### Configure Access Credentials
-
-After setting up the S3 FileSystem wrapper, you need to make sure that Flink is allowed to access your S3 buckets.
-
-##### Identity and Access Management (IAM) (Recommended)
-
-The recommended way of setting up credentials on AWS is via [Identity and Access Management (IAM)](https://docs.aws.amazon.com/IAM/latest/UserGuide/introduction.html). You can use IAM features to securely give Flink instances the credentials that they need in order to access S3 buckets. Details about how to do this are beyond the scope of this documentation. Please refer to the AWS user guide. What you are looking for are [IAM Roles](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/iam-roles-for-amazon-ec2.html).
-
-If you set this up correctly, you can manage access to S3 within AWS and don't need to distribute any access keys to Flink.
-
-##### Access Keys (Discouraged)
-
-Access to S3 can be granted via your **access and secret key pair**. Please note that this is discouraged since the [introduction of IAM roles](https://blogs.aws.amazon.com/security/post/Tx1XG3FX6VMU6O5/A-safer-way-to-distribute-AWS-credentials-to-EC2).
-
-You need to configure both `s3.access-key` and `s3.secret-key` in Flink's `flink-conf.yaml`:
-
-{% highlight yaml %}
-s3.access-key: your-access-key
-s3.secret-key: your-secret-key
-{% endhighlight %}
-
-{% top %}
-
-### Hadoop-provided S3 file systems - manual setup
+### Hadoop-provided S3 file systems
 
 {% panel **Note:** You don't have to configure this manually if you are running [Flink on EMR](#emr-elastic-mapreduce). %}
 
-This setup is a bit more complex and we recommend using our shaded Hadoop/Presto file systems
-instead (see above) unless required otherwise, e.g. for using S3 as YARN's resource storage dir
+Apache Flink provides native [S3 FileSystem's](../filesystems/s3.html) out of the box and we recomend using them unless required otherwise, e.g. for using S3 as YARN's resource storage dir
 via the `fs.defaultFS` configuration property in Hadoop's `core-site.xml`.
 
 #### Set S3 FileSystem

docs/ops/filesystems/common.md

@@ -0,0 +1,68 @@
+---
+title: "Common Configurations"
+nav-parent_id: filesystems
+nav-pos: 0
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you 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
+
+ https://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.
+-->
+
+Apache Flink provides a number of common configuration settings that work across all file system implementations. 
+
+* This will be replaced by the TOC
+{:toc}
+
+## Default File System
+
+If paths to files do not explicitly specify a file system scheme (and authority), a default scheme (and authority) will be used.
+
+{% highlight yaml %}
+fs.default-scheme: <default-fs>
+{% endhighlight %}
+
+For example, if the default file system configured as `fs.default-scheme: hdfs:https://localhost:9000/`, then a file path of
+`/user/hugo/in.txt` is interpreted as `hdfs:https://localhost:9000/user/hugo/in.txt`.
+
+## Connection limiting
+
+You can limit the total number of connections that a file system can concurrently open. This is useful when the file system cannot handle a large number
+of concurrent reads / writes or open connections at the same time.
+
+For example, very small HDFS clusters with few RPC handlers can sometimes be overwhelmed by a large Flink job trying to build up many connections during a checkpoint.
+
+To limit a specific file system's connections, add the following entries to the Flink configuration. The file system to be limited is identified by
+its scheme.
+
+{% highlight yaml %}
+fs.<scheme>.limit.total: (number, 0/-1 mean no limit)
+fs.<scheme>.limit.input: (number, 0/-1 mean no limit)
+fs.<scheme>.limit.output: (number, 0/-1 mean no limit)
+fs.<scheme>.limit.timeout: (milliseconds, 0 means infinite)
+fs.<scheme>.limit.stream-timeout: (milliseconds, 0 means infinite)
+{% endhighlight %}
+
+You can limit the number if input/output connections (streams) separately (`fs.<scheme>.limit.input` and `fs.<scheme>.limit.output`), as well as impose a limit on
+the total number of concurrent streams (`fs.<scheme>.limit.total`). If the file system tries to open more streams, the operation will block until some streams are closed.
+If the opening of the stream takes longer than `fs.<scheme>.limit.timeout`, the stream opening will fail.
+
+To prevent inactive streams from taking up the complete pool (preventing new connections to be opened), you can add an inactivity timeout for streams:
+`fs.<scheme>.limit.stream-timeout`. If a stream does not read/write any bytes for at least that amount of time, it is forcibly closed.
+
+These limits are enforced per TaskManager, so each TaskManager in a Flink application or cluster will open up to that number of connections.
+In addition, the limits are also only enforced per FileSystem instance. Because File Systems are created per scheme and authority, different
+authorities will have their own connection pool. For example `hdfs:https://myhdfs:50010/` and `hdfs:https://anotherhdfs:4399/` will have separate pools.