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From ij...@apache.org
Subject [04/11] kafka-site git commit: 0.11.0.0 docs
Date Thu, 08 Jun 2017 13:31:21 GMT
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/1e786061/0110/protocol.html
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+<!--
+ 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
+
+    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
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+
+<!--#include virtual="../includes/_header.htm" -->
+<!--#include virtual="../includes/_top.htm" -->
+<div class="content">
+    <!--#include virtual="../includes/_nav.htm" -->
+    <div class="right">
+        <h1>Kafka protocol guide</h1>
+
+<p>This document covers the wire protocol implemented in Kafka. It is meant to give
a readable guide to the protocol that covers the available requests, their binary format,
and the proper way to make use of them to implement a client. This document assumes you understand
the basic design and terminology described <a href="https://kafka.apache.org/documentation.html#design">here</a></p>
+
+<ul class="toc">
+    <li><a href="#protocol_preliminaries">Preliminaries</a>
+        <ul>
+            <li><a href="#protocol_network">Network</a>
+            <li><a href="#protocol_partitioning">Partitioning and bootstrapping</a>
+            <li><a href="#protocol_partitioning_strategies">Partitioning Strategies</a>
+            <li><a href="#protocol_batching">Batching</a>
+            <li><a href="#protocol_compatibility">Versioning and Compatibility</a>
+        </ul>
+    </li>
+    <li><a href="#protocol_details">The Protocol</a>
+        <ul>
+            <li><a href="#protocol_types">Protocol Primitive Types</a>
+            <li><a href="#protocol_grammar">Notes on reading the request format
grammars</a>
+            <li><a href="#protocol_common">Common Request and Response Structure</a>
+            <li><a href="#protocol_message_sets">Message Sets</a>
+        </ul>
+    </li>
+    <li><a href="#protocol_constants">Constants</a>
+        <ul>
+            <li><a href="#protocol_error_codes">Error Codes</a>
+            <li><a href="#protocol_api_keys">Api Keys</a>
+        </ul>
+    </li>
+    <li><a href="#protocol_messages">The Messages</a></li>
+    <li><a href="#protocol_philosophy">Some Common Philosophical Questions</a></li>
+</ul>
+
+<h4><a id="protocol_preliminaries" href="#protocol_preliminaries">Preliminaries</a></h4>
+
+<h5><a id="protocol_network" href="#protocol_network">Network</a></h5>
+
+<p>Kafka uses a binary protocol over TCP. The protocol defines all apis as request
response message pairs. All messages are size delimited and are made up of the following primitive
types.</p>
+
+<p>The client initiates a socket connection and then writes a sequence of request messages
and reads back the corresponding response message. No handshake is required on connection
or disconnection. TCP is happier if you maintain persistent connections used for many requests
to amortize the cost of the TCP handshake, but beyond this penalty connecting is pretty cheap.</p>
+
+<p>The client will likely need to maintain a connection to multiple brokers, as data
is partitioned and the clients will need to talk to the server that has their data. However
it should not generally be necessary to maintain multiple connections to a single broker from
a single client instance (i.e. connection pooling).</p>
+
+<p>The server guarantees that on a single TCP connection, requests will be processed
in the order they are sent and responses will return in that order as well. The broker's request
processing allows only a single in-flight request per connection in order to guarantee this
ordering. Note that clients can (and ideally should) use non-blocking IO to implement request
pipelining and achieve higher throughput. i.e., clients can send requests even while awaiting
responses for preceding requests since the outstanding requests will be buffered in the underlying
OS socket buffer. All requests are initiated by the client, and result in a corresponding
response message from the server except where noted.</p>
+
+<p>The server has a configurable maximum limit on request size and any request that
exceeds this limit will result in the socket being disconnected.</p>
+
+<h5><a id="protocol_partitioning" href="#protocol_partitioning">Partitioning
and bootstrapping</a></h5>
+
+<p>Kafka is a partitioned system so not all servers have the complete data set. Instead
recall that topics are split into a pre-defined number of partitions, P, and each partition
is replicated with some replication factor, N. Topic partitions themselves are just ordered
"commit logs" numbered 0, 1, ..., P.</p>
+
+<p>All systems of this nature have the question of how a particular piece of data is
assigned to a particular partition. Kafka clients directly control this assignment, the brokers
themselves enforce no particular semantics of which messages should be published to a particular
partition. Rather, to publish messages the client directly addresses messages to a particular
partition, and when fetching messages, fetches from a particular partition. If two clients
want to use the same partitioning scheme they must use the same method to compute the mapping
of key to partition.</p>
+
+<p>These requests to publish or fetch data must be sent to the broker that is currently
acting as the leader for a given partition. This condition is enforced by the broker, so a
request for a particular partition to the wrong broker will result in an the NotLeaderForPartition
error code (described below).</p>
+
+<p>How can the client find out which topics exist, what partitions they have, and which
brokers currently host those partitions so that it can direct its requests to the right hosts?
This information is dynamic, so you can't just configure each client with some static mapping
file. Instead all Kafka brokers can answer a metadata request that describes the current state
of the cluster: what topics there are, which partitions those topics have, which broker is
the leader for those partitions, and the host and port information for these brokers.</p>
+
+<p>In other words, the client needs to somehow find one broker and that broker will
tell the client about all the other brokers that exist and what partitions they host. This
first broker may itself go down so the best practice for a client implementation is to take
a list of two or three urls to bootstrap from. The user can then choose to use a load balancer
or just statically configure two or three of their kafka hosts in the clients.</p>
+
+<p>The client does not need to keep polling to see if the cluster has changed; it can
fetch metadata once when it is instantiated cache that metadata until it receives an error
indicating that the metadata is out of date. This error can come in two forms: (1) a socket
error indicating the client cannot communicate with a particular broker, (2) an error code
in the response to a request indicating that this broker no longer hosts the partition for
which data was requested.</p>
+<ol>
+    <li>Cycle through a list of "bootstrap" kafka urls until we find one we can connect
to. Fetch cluster metadata.</li>
+    <li>Process fetch or produce requests, directing them to the appropriate broker
based on the topic/partitions they send to or fetch from.</li>
+    <li>If we get an appropriate error, refresh the metadata and try again.</li>
+</ol>
+
+<h5><a id="protocol_partitioning_strategies" href="#protocol_partitioning_strategies">Partitioning
Strategies</a></h5>
+
+<p>As mentioned above the assignment of messages to partitions is something the producing
client controls. That said, how should this functionality be exposed to the end-user?</p>
+
+<p>Partitioning really serves two purposes in Kafka:</p>
+<ol>
+    <li>It balances data and request load over brokers</li>
+    <li>It serves as a way to divvy up processing among consumer processes while allowing
local state and preserving order within the partition. We call this semantic partitioning.</li>
+</ol>
+
+<p>For a given use case you may care about only one of these or both.</p>
+
+<p>To accomplish simple load balancing a simple approach would be for the client to
just round robin requests over all brokers. Another alternative, in an environment where there
are many more producers than brokers, would be to have each client chose a single partition
at random and publish to that. This later strategy will result in far fewer TCP connections.</p>
+
+<p>Semantic partitioning means using some key in the message to assign messages to
partitions. For example if you were processing a click message stream you might want to partition
the stream by the user id so that all data for a particular user would go to a single consumer.
To accomplish this the client can take a key associated with the message and use some hash
of this key to choose the partition to which to deliver the message.</p>
+
+<h5><a id="protocol_batching" href="#protocol_batching">Batching</a></h5>
+
+<p>Our apis encourage batching small things together for efficiency. We have found
this is a very significant performance win. Both our API to send messages and our API to fetch
messages always work with a sequence of messages not a single message to encourage this. A
clever client can make use of this and support an "asynchronous" mode in which it batches
together messages sent individually and sends them in larger clumps. We go even further with
this and allow the batching across multiple topics and partitions, so a produce request may
contain data to append to many partitions and a fetch request may pull data from many partitions
all at once.</p>
+
+<p>The client implementer can choose to ignore this and send everything one at a time
if they like.</p>
+
+<h5><a id="protocol_compatibility" href="#protocol_compatibility">Versioning
and Compatibility</a></h5>
+
+<p>The protocol is designed to enable incremental evolution in a backward compatible
fashion. Our versioning is on a per API basis, each version consisting of a request and response
pair. Each request contains an API key that identifies the API being invoked and a version
number that indicates the format of the request and the expected format of the response.</p>
+
+<p>The intention is that clients will support a range of API versions. When communicating
with a particular broker, a given client should use the highest API version supported by both
and indicate this version in their requests.</p>
+
+<p>The server will reject requests with a version it does not support, and will always
respond to the client with exactly the protocol format it expects based on the version it
included in its request. The intended upgrade path is that new features would first be rolled
out on the server (with the older clients not making use of them) and then as newer clients
are deployed these new features would gradually be taken advantage of.</p>
+
+<p>Our goal is primarily to allow API evolution in an environment where downtime is
not allowed and clients and servers cannot all be changed at once.</p>
+
+<p>Currently all versions are baselined at 0, as we evolve these APIs we will indicate
the format for each version individually.</p>
+
+<h5><a id="api_versions" href="#api_versions">Retrieving Supported API versions</a></h5>
+<p>In order to work against multiple broker versions, clients need to know what versions
of various APIs a
+    broker supports. The broker exposes this information since 0.10.0.0 as described in <a
href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-35+-+Retrieving+protocol+version">KIP-35</a>.
+    Clients should use the supported API versions information to choose the highest API version
supported by both client and broker. If no such version
+    exists, an error should be reported to the user.</p>
+<p>The following sequence may be used by a client to obtain supported API versions
from a broker.</p>
+<ol>
+    <li>Client sends <code>ApiVersionsRequest</code> to a broker after
connection has been established with the broker. If SSL is enabled,
+        this happens after SSL connection has been established.</li>
+    <li>On receiving <code>ApiVersionsRequest</code>, a broker returns
its full list of supported ApiKeys and
+        versions regardless of current authentication state (e.g., before SASL authentication
on an SASL listener, do note that no
+        Kafka protocol requests may take place on a SSL listener before the SSL handshake
is finished). If this is considered to
+        leak information about the broker version a workaround is to use SSL with client
authentication which is performed at an
+        earlier stage of the connection where the <code>ApiVersionRequest</code>
is not available. Also, note that broker versions older
+        than 0.10.0.0 do not support this API and will either ignore the request or close
connection in response to the request.</li>
+    <li>If multiple versions of an API are supported by broker and client, clients
are recommended to use the latest version supported
+        by the broker and itself.</li>
+    <li>Deprecation of a protocol version is done by marking an API version as deprecated
in the protocol documentation.</li>
+    <li>Supported API versions obtained from a broker are only valid for the connection
on which that information is obtained.
+        In the event of disconnection, the client should obtain the information from the
broker again, as the broker might have been
+        upgraded/downgraded in the mean time.</li>
+</ol>
+
+<h5><a id="sasl_handshake" href="#sasl_handshake">SASL Authentication Sequence</a></h5>
+<p>The following sequence is used for SASL authentication:
+<ol>
+  <li>Kafka <code>ApiVersionsRequest</code> may be sent by the client to
obtain the version ranges of requests supported by the broker. This is optional.</li>
+  <li>Kafka <code>SaslHandshakeRequest</code> containing the SASL mechanism
for authentication is sent by the client. If the requested mechanism is not enabled
+    in the server, the server responds with the list of supported mechanisms and closes the
client connection. If the mechanism is enabled
+    in the server, the server sends a successful response and continues with SASL authentication.
+  <li>The actual SASL authentication is now performed. A series of SASL client and
server tokens corresponding to the mechanism are sent as opaque
+    packets. These packets contain a 32-bit size followed by the token as defined by the
protocol for the SASL mechanism.
+  <li>If authentication succeeds, subsequent packets are handled as Kafka API requests.
Otherwise, the client connection is closed.
+</ol>
+<p>For interoperability with 0.9.0.x clients, the first packet received by the server
is handled as a SASL/GSSAPI client token if it is not a valid
+Kafka request. SASL/GSSAPI authentication is performed starting with this packet, skipping
the first two steps above.</p>
+
+
+<h4><a id="protocol_details" href="#protocol_details">The Protocol</a></h4>
+
+<h5><a id="protocol_types" href="#protocol_types">Protocol Primitive Types</a></h5>
+
+<p>The protocol is built out of the following primitive types.</p>
+
+<p><b>Fixed Width Primitives</b><p>
+
+<p>int8, int16, int32, int64 - Signed integers with the given precision (in bits) stored
in big endian order.</p>
+
+<p><b>Variable Length Primitives</b><p>
+
+<p>bytes, string - These types consist of a signed integer giving a length N followed
by N bytes of content. A length of -1 indicates null. string uses an int16 for its size, and
bytes uses an int32.</p>
+
+<p><b>Arrays</b><p>
+
+<p>This is a notation for handling repeated structures. These will always be encoded
as an int32 size containing the length N followed by N repetitions of the structure which
can itself be made up of other primitive types. In the BNF grammars below we will show an
array of a structure foo as [foo].</p>
+
+<h5><a id="protocol_grammar" href="#protocol_grammar">Notes on reading the request
format grammars</a></h5>
+
+<p>The <a href="https://en.wikipedia.org/wiki/Backus%E2%80%93Naur_Form">BNF</a>s
below give an exact context free grammar for the request and response binary format. The BNF
is intentionally not compact in order to give human-readable name. As always in a BNF a sequence
of productions indicates concatenation. When there are multiple possible productions these
are separated with '|' and may be enclosed in parenthesis for grouping. The top-level definition
is always given first and subsequent sub-parts are indented.</p>
+
+<h5><a id="protocol_common" href="#protocol_common">Common Request and Response
Structure</a></h5>
+
+<p>All requests and responses originate from the following grammar which will be incrementally
describe through the rest of this document:</p>
+
+<pre>
+RequestOrResponse => Size (RequestMessage | ResponseMessage)
+Size => int32
+</pre>
+
+<table class="data-table"><tbody>
+<tr><th>Field</th><th>Description</th></tr>
+<tr><td>message_size</td><td>The message_size field gives the size
of the subsequent request or response message in bytes. The client can read requests by first
reading this 4 byte size as an integer N, and then reading and parsing the subsequent N bytes
of the request.</td></tr>
+</table>
+
+<h5><a id="protocol_message_sets" href="#protocol_message_sets">Message Sets</a></h5>
+
+<p>A description of the message set format can be found <a href="https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol#AGuideToTheKafkaProtocol-Messagesets">here</a>.
(KAFKA-3368)</p>
+
+<h4><a id="protocol_constants" href="#protocol_constants">Constants</a></h4>
+
+<h5><a id="protocol_error_codes" href="#protocol_error_codes">Error Codes</a></h5>
+<p>We use numeric codes to indicate what problem occurred on the server. These can
be translated by the client into exceptions or whatever the appropriate error handling mechanism
in the client language. Here is a table of the error codes currently in use:</p>
+<!--#include virtual="generated/protocol_errors.html" -->
+
+<h5><a id="protocol_api_keys" href="#protocol_api_keys">Api Keys</a></h5>
+<p>The following are the numeric codes that the ApiKey in the request can take for
each of the below request types.</p>
+<!--#include virtual="generated/protocol_api_keys.html" -->
+
+<h4><a id="protocol_messages" href="#protocol_messages">The Messages</a></h4>
+
+<p>This section gives details on each of the individual API Messages, their usage,
their binary format, and the meaning of their fields.</p>
+<!--#include virtual="generated/protocol_messages.html" -->
+
+<h4><a id="protocol_philosophy" href="#protocol_philosophy">Some Common Philosophical
Questions</a></h4>
+
+<p>Some people have asked why we don't use HTTP. There are a number of reasons, the
best is that client implementors can make use of some of the more advanced TCP features--the
ability to multiplex requests, the ability to simultaneously poll many connections, etc. We
have also found HTTP libraries in many languages to be surprisingly shabby.</p>
+
+<p>Others have asked if maybe we shouldn't support many different protocols. Prior
experience with this was that it makes it very hard to add and test new features if they have
to be ported across many protocol implementations. Our feeling is that most users don't really
see multiple protocols as a feature, they just want a good reliable client in the language
of their choice.</p>
+
+<p>Another question is why we don't adopt XMPP, STOMP, AMQP or an existing protocol.
The answer to this varies by protocol, but in general the problem is that the protocol does
determine large parts of the implementation and we couldn't do what we are doing if we didn't
have control over the protocol. Our belief is that it is possible to do better than existing
messaging systems have in providing a truly distributed messaging system, and to do this we
need to build something that works differently.</p>
+
+<p>A final question is why we don't use a system like Protocol Buffers or Thrift to
define our request messages. These packages excel at helping you to managing lots and lots
of serialized messages. However we have only a few messages. Support across languages is somewhat
spotty (depending on the package). Finally the mapping between binary log format and wire
protocol is something we manage somewhat carefully and this would not be possible with these
systems. Finally we prefer the style of versioning APIs explicitly and checking this to inferring
new values as nulls as it allows more nuanced control of compatibility.</p>
+
+    <script>
+        // Show selected style on nav item
+        $(function() { $('.b-nav__project').addClass('selected'); });
+    </script>
+
+<!--#include virtual="../includes/_footer.htm" -->

http://git-wip-us.apache.org/repos/asf/kafka-site/blob/1e786061/0110/quickstart.html
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+<!--
+ 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
+
+    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.
+-->
+
+<script><!--#include virtual="js/templateData.js" --></script>
+
+<script id="quickstart-template" type="text/x-handlebars-template">
+<p>
+This tutorial assumes you are starting fresh and have no existing Kafka or ZooKeeper data.
+Since Kafka console scripts are different for Unix-based and Windows platforms, on Windows
platforms use <code>bin\windows\</code> instead of <code>bin/</code>,
and change the script extension to <code>.bat</code>.
+</p>
+
+<h4><a id="quickstart_download" href="#quickstart_download">Step 1: Download
the code</a></h4>
+
+<a href="https://www.apache.org/dyn/closer.cgi?path=/kafka/0.10.2.0/kafka_2.11-0.10.2.0.tgz"
title="Kafka downloads">Download</a> the 0.10.2.0 release and un-tar it.
+
+<pre class="brush: bash;">
+&gt; tar -xzf kafka_2.11-0.10.2.0.tgz
+&gt; cd kafka_2.11-0.10.2.0
+</pre>
+
+<h4><a id="quickstart_startserver" href="#quickstart_startserver">Step 2: Start
the server</a></h4>
+
+<p>
+Kafka uses ZooKeeper so you need to first start a ZooKeeper server if you don't already have
one. You can use the convenience script packaged with kafka to get a quick-and-dirty single-node
ZooKeeper instance.
+</p>
+
+<pre class="brush: bash;">
+&gt; bin/zookeeper-server-start.sh config/zookeeper.properties
+[2013-04-22 15:01:37,495] INFO Reading configuration from: config/zookeeper.properties (org.apache.zookeeper.server.quorum.QuorumPeerConfig)
+...
+</pre>
+
+<p>Now start the Kafka server:</p>
+<pre class="brush: bash;">
+&gt; bin/kafka-server-start.sh config/server.properties
+[2013-04-22 15:01:47,028] INFO Verifying properties (kafka.utils.VerifiableProperties)
+[2013-04-22 15:01:47,051] INFO Property socket.send.buffer.bytes is overridden to 1048576
(kafka.utils.VerifiableProperties)
+...
+</pre>
+
+<h4><a id="quickstart_createtopic" href="#quickstart_createtopic">Step 3: Create
a topic</a></h4>
+
+<p>Let's create a topic named "test" with a single partition and only one replica:</p>
+<pre class="brush: bash;">
+&gt; bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions
1 --topic test
+</pre>
+
+<p>We can now see that topic if we run the list topic command:</p>
+<pre class="brush: bash;">
+&gt; bin/kafka-topics.sh --list --zookeeper localhost:2181
+test
+</pre>
+<p>Alternatively, instead of manually creating topics you can also configure your brokers
to auto-create topics when a non-existent topic is published to.</p>
+
+<h4><a id="quickstart_send" href="#quickstart_send">Step 4: Send some messages</a></h4>
+
+<p>Kafka comes with a command line client that will take input from a file or from
standard input and send it out as messages to the Kafka cluster. By default, each line will
be sent as a separate message.</p>
+<p>
+Run the producer and then type a few messages into the console to send to the server.</p>
+
+<pre class="brush: bash;">
+&gt; bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test
+This is a message
+This is another message
+</pre>
+
+<h4><a id="quickstart_consume" href="#quickstart_consume">Step 5: Start a consumer</a></h4>
+
+<p>Kafka also has a command line consumer that will dump out messages to standard output.</p>
+
+<pre class="brush: bash;">
+&gt; bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic test --from-beginning
+This is a message
+This is another message
+</pre>
+<p>
+If you have each of the above commands running in a different terminal then you should now
be able to type messages into the producer terminal and see them appear in the consumer terminal.
+</p>
+<p>
+All of the command line tools have additional options; running the command with no arguments
will display usage information documenting them in more detail.
+</p>
+
+<h4><a id="quickstart_multibroker" href="#quickstart_multibroker">Step 6: Setting
up a multi-broker cluster</a></h4>
+
+<p>So far we have been running against a single broker, but that's no fun. For Kafka,
a single broker is just a cluster of size one, so nothing much changes other than starting
a few more broker instances. But just to get feel for it, let's expand our cluster to three
nodes (still all on our local machine).</p>
+<p>
+First we make a config file for each of the brokers (on Windows use the <code>copy</code>
command instead):
+</p>
+<pre class="brush: bash;">
+&gt; cp config/server.properties config/server-1.properties
+&gt; cp config/server.properties config/server-2.properties
+</pre>
+
+<p>
+Now edit these new files and set the following properties:
+</p>
+<pre class="brush: text;">
+
+config/server-1.properties:
+    broker.id=1
+    listeners=PLAINTEXT://:9093
+    log.dir=/tmp/kafka-logs-1
+
+config/server-2.properties:
+    broker.id=2
+    listeners=PLAINTEXT://:9094
+    log.dir=/tmp/kafka-logs-2
+</pre>
+<p>The <code>broker.id</code> property is the unique and permanent name
of each node in the cluster. We have to override the port and log directory only because we
are running these all on the same machine and we want to keep the brokers from all trying
to register on the same port or overwrite each other's data.</p>
+<p>
+We already have Zookeeper and our single node started, so we just need to start the two new
nodes:
+</p>
+<pre class="brush: bash;">
+&gt; bin/kafka-server-start.sh config/server-1.properties &amp;
+...
+&gt; bin/kafka-server-start.sh config/server-2.properties &amp;
+...
+</pre>
+
+<p>Now create a new topic with a replication factor of three:</p>
+<pre class="brush: bash;">
+&gt; bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 3 --partitions
1 --topic my-replicated-topic
+</pre>
+
+<p>Okay but now that we have a cluster how can we know which broker is doing what?
To see that run the "describe topics" command:</p>
+<pre class="brush: bash;">
+&gt; bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic my-replicated-topic
+Topic:my-replicated-topic	PartitionCount:1	ReplicationFactor:3	Configs:
+	Topic: my-replicated-topic	Partition: 0	Leader: 1	Replicas: 1,2,0	Isr: 1,2,0
+</pre>
+<p>Here is an explanation of output. The first line gives a summary of all the partitions,
each additional line gives information about one partition. Since we have only one partition
for this topic there is only one line.</p>
+<ul>
+  <li>"leader" is the node responsible for all reads and writes for the given partition.
Each node will be the leader for a randomly selected portion of the partitions.
+  <li>"replicas" is the list of nodes that replicate the log for this partition regardless
of whether they are the leader or even if they are currently alive.
+  <li>"isr" is the set of "in-sync" replicas. This is the subset of the replicas list
that is currently alive and caught-up to the leader.
+</ul>
+<p>Note that in my example node 1 is the leader for the only partition of the topic.</p>
+<p>
+We can run the same command on the original topic we created to see where it is:
+</p>
+<pre class="brush: bash;">
+&gt; bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic test
+Topic:test	PartitionCount:1	ReplicationFactor:1	Configs:
+	Topic: test	Partition: 0	Leader: 0	Replicas: 0	Isr: 0
+</pre>
+<p>So there is no surprise there&mdash;the original topic has no replicas and is
on server 0, the only server in our cluster when we created it.</p>
+<p>
+Let's publish a few messages to our new topic:
+</p>
+<pre class="brush: bash;">
+&gt; bin/kafka-console-producer.sh --broker-list localhost:9092 --topic my-replicated-topic
+...
+my test message 1
+my test message 2
+^C
+</pre>
+<p>Now let's consume these messages:</p>
+<pre class="brush: bash;">
+&gt; bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --from-beginning
--topic my-replicated-topic
+...
+my test message 1
+my test message 2
+^C
+</pre>
+
+<p>Now let's test out fault-tolerance. Broker 1 was acting as the leader so let's kill
it:</p>
+<pre class="brush: bash;">
+&gt; ps aux | grep server-1.properties
+7564 ttys002    0:15.91 /System/Library/Frameworks/JavaVM.framework/Versions/1.8/Home/bin/java...
+&gt; kill -9 7564
+</pre>
+
+On Windows use:
+<pre class="brush: bash;">
+&gt; wmic process get processid,caption,commandline | find "java.exe" | find "server-1.properties"
+java.exe    java  -Xmx1G -Xms1G -server -XX:+UseG1GC ... build\libs\kafka_2.11-0.10.2.0.jar"
 kafka.Kafka config\server-1.properties    644
+&gt; taskkill /pid 644 /f
+</pre>
+
+<p>Leadership has switched to one of the slaves and node 1 is no longer in the in-sync
replica set:</p>
+
+<pre class="brush: bash;">
+&gt; bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic my-replicated-topic
+Topic:my-replicated-topic	PartitionCount:1	ReplicationFactor:3	Configs:
+	Topic: my-replicated-topic	Partition: 0	Leader: 2	Replicas: 1,2,0	Isr: 2,0
+</pre>
+<p>But the messages are still available for consumption even though the leader that
took the writes originally is down:</p>
+<pre class="brush: bash;">
+&gt; bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --from-beginning
--topic my-replicated-topic
+...
+my test message 1
+my test message 2
+^C
+</pre>
+
+
+<h4><a id="quickstart_kafkaconnect" href="#quickstart_kafkaconnect">Step 7: Use
Kafka Connect to import/export data</a></h4>
+
+<p>Writing data from the console and writing it back to the console is a convenient
place to start, but you'll probably want
+to use data from other sources or export data from Kafka to other systems. For many systems,
instead of writing custom
+integration code you can use Kafka Connect to import or export data.</p>
+
+<p>Kafka Connect is a tool included with Kafka that imports and exports data to Kafka.
It is an extensible tool that runs
+<i>connectors</i>, which implement the custom logic for interacting with an external
system. In this quickstart we'll see
+how to run Kafka Connect with simple connectors that import data from a file to a Kafka topic
and export data from a
+Kafka topic to a file.</p>
+
+<p>First, we'll start by creating some seed data to test with:</p>
+
+<pre class="brush: bash;">
+&gt; echo -e "foo\nbar" > test.txt
+</pre>
+
+<p>Next, we'll start two connectors running in <i>standalone</i> mode,
which means they run in a single, local, dedicated
+process. We provide three configuration files as parameters. The first is always the configuration
for the Kafka Connect
+process, containing common configuration such as the Kafka brokers to connect to and the
serialization format for data.
+The remaining configuration files each specify a connector to create. These files include
a unique connector name, the connector
+class to instantiate, and any other configuration required by the connector.</p>
+
+<pre class="brush: bash;">
+&gt; bin/connect-standalone.sh config/connect-standalone.properties config/connect-file-source.properties
config/connect-file-sink.properties
+</pre>
+
+<p>
+These sample configuration files, included with Kafka, use the default local cluster configuration
you started earlier
+and create two connectors: the first is a source connector that reads lines from an input
file and produces each to a Kafka topic
+and the second is a sink connector that reads messages from a Kafka topic and produces each
as a line in an output file.
+</p>
+
+<p>
+During startup you'll see a number of log messages, including some indicating that the connectors
are being instantiated.
+Once the Kafka Connect process has started, the source connector should start reading lines
from <code>test.txt</code> and
+producing them to the topic <code>connect-test</code>, and the sink connector
should start reading messages from the topic <code>connect-test</code>
+and write them to the file <code>test.sink.txt</code>. We can verify the data
has been delivered through the entire pipeline
+by examining the contents of the output file:
+</p>
+
+
+<pre class="brush: bash;">
+&gt; cat test.sink.txt
+foo
+bar
+</pre>
+
+<p>
+Note that the data is being stored in the Kafka topic <code>connect-test</code>,
so we can also run a console consumer to see the
+data in the topic (or use custom consumer code to process it):
+</p>
+
+
+<pre class="brush: bash;">
+&gt; bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic connect-test
--from-beginning
+{"schema":{"type":"string","optional":false},"payload":"foo"}
+{"schema":{"type":"string","optional":false},"payload":"bar"}
+...
+</pre>
+
+<p>The connectors continue to process data, so we can add data to the file and see
it move through the pipeline:</p>
+
+<pre class="brush: bash;">
+&gt; echo "Another line" >> test.txt
+</pre>
+
+<p>You should see the line appear in the console consumer output and in the sink file.</p>
+
+<h4><a id="quickstart_kafkastreams" href="#quickstart_kafkastreams">Step 8: Use
Kafka Streams to process data</a></h4>
+
+<p>
+Kafka Streams is a client library of Kafka for real-time stream processing and analyzing
data stored in Kafka brokers.
+This quickstart example will demonstrate how to run a streaming application coded in this
library. Here is the gist
+of the <code><a href="https://github.com/apache/kafka/blob/{{dotVersion}}/streams/examples/src/main/java/org/apache/kafka/streams/examples/wordcount/WordCountDemo.java">WordCountDemo</a></code>
example code (converted to use Java 8 lambda expressions for easy reading).
+</p>
+<pre class="brush: bash;">
+// Serializers/deserializers (serde) for String and Long types
+final Serde&lt;String&gt; stringSerde = Serdes.String();
+final Serde&lt;Long&gt; longSerde = Serdes.Long();
+
+// Construct a `KStream` from the input topic ""streams-file-input", where message values
+// represent lines of text (for the sake of this example, we ignore whatever may be stored
+// in the message keys).
+KStream&lt;String, String&gt; textLines = builder.stream(stringSerde, stringSerde,
"streams-file-input");
+
+KTable&lt;String, Long&gt; wordCounts = textLines
+    // Split each text line, by whitespace, into words.
+    .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
+
+    // Group the text words as message keys
+    .groupBy((key, value) -> value)
+
+    // Count the occurrences of each word (message key).
+    .count("Counts")
+
+// Store the running counts as a changelog stream to the output topic.
+wordCounts.to(stringSerde, longSerde, "streams-wordcount-output");
+</pre>
+
+<p>
+It implements the WordCount
+algorithm, which computes a word occurrence histogram from the input text. However, unlike
other WordCount examples
+you might have seen before that operate on bounded data, the WordCount demo application behaves
slightly differently because it is
+designed to operate on an <b>infinite, unbounded stream</b> of data. Similar
to the bounded variant, it is a stateful algorithm that
+tracks and updates the counts of words. However, since it must assume potentially
+unbounded input data, it will periodically output its current state and results while continuing
to process more data
+because it cannot know when it has processed "all" the input data.
+</p>
+<p>
+As the first step, we will prepare input data to a Kafka topic, which will subsequently be
processed by a Kafka Streams application.
+</p>
+
+<!--
+<pre>
+&gt; <b>./bin/kafka-topics --create \</b>
+            <b>--zookeeper localhost:2181 \</b>
+            <b>--replication-factor 1 \</b>
+            <b>--partitions 1 \</b>
+            <b>--topic streams-file-input</b>
+
+</pre>
+
+-->
+
+<pre class="brush: bash;">
+&gt; echo -e "all streams lead to kafka\nhello kafka streams\njoin kafka summit" >
file-input.txt
+</pre>
+Or on Windows:
+<pre class="brush: bash;">
+&gt; echo all streams lead to kafka> file-input.txt
+&gt; echo hello kafka streams>> file-input.txt
+&gt; echo|set /p=join kafka summit>> file-input.txt
+</pre>
+
+<p>
+Next, we send this input data to the input topic named <b>streams-file-input</b>
using the console producer,
+which reads the data from STDIN line-by-line, and publishes each line as a separate Kafka
message with null key and value encoded a string to the topic (in practice,
+stream data will likely be flowing continuously into Kafka where the application will be
up and running):
+</p>
+
+<pre class="brush: bash;">
+&gt; bin/kafka-topics.sh --create \
+    --zookeeper localhost:2181 \
+    --replication-factor 1 \
+    --partitions 1 \
+    --topic streams-file-input
+</pre>
+
+
+<pre class="brush: bash;">
+&gt; bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-file-input
< file-input.txt
+</pre>
+
+<p>
+We can now run the WordCount demo application to process the input data:
+</p>
+
+<pre class="brush: bash;">
+&gt; bin/kafka-run-class.sh org.apache.kafka.streams.examples.wordcount.WordCountDemo
+</pre>
+
+<p>
+The demo application will read from the input topic <b>streams-file-input</b>,
perform the computations of the WordCount algorithm on each of the read messages,
+and continuously write its current results to the output topic <b>streams-wordcount-output</b>.
+Hence there won't be any STDOUT output except log entries as the results are written back
into in Kafka.
+The demo will run for a few seconds and then, unlike typical stream processing applications,
terminate automatically.
+</p>
+<p>
+We can now inspect the output of the WordCount demo application by reading from its output
topic:
+</p>
+
+<pre class="brush: bash;">
+&gt; bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 \
+    --topic streams-wordcount-output \
+    --from-beginning \
+    --formatter kafka.tools.DefaultMessageFormatter \
+    --property print.key=true \
+    --property print.value=true \
+    --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer
\
+    --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer
+</pre>
+
+<p>
+with the following output data being printed to the console:
+</p>
+
+<pre class="brush: bash;">
+all     1
+lead    1
+to      1
+hello   1
+streams 2
+join    1
+kafka   3
+summit  1
+</pre>
+
+<p>
+Here, the first column is the Kafka message key in <code>java.lang.String</code>
format, and the second column is the message value in <code>java.lang.Long</code>
format.
+Note that the output is actually a continuous stream of updates, where each data record (i.e.
each line in the original output above) is
+an updated count of a single word, aka record key such as "kafka". For multiple records with
the same key, each later record is an update of the previous one.
+</p>
+
+<p>
+The two diagrams below illustrate what is essentially happening behind the scenes.
+The first column shows the evolution of the current state of the <code>KTable&lt;String,
Long&gt;</code> that is counting word occurrences for <code>count</code>.
+The second column shows the change records that result from state updates to the KTable and
that are being sent to the output Kafka topic <b>streams-wordcount-output</b>.
+</p>
+
+<img src="/{{version}}/images/streams-table-updates-02.png" style="float: right; width:
25%;">
+<img src="/{{version}}/images/streams-table-updates-01.png" style="float: right; width:
25%;">
+
+<p>
+First the text line “all streams lead to kafka” is being processed.
+The <code>KTable</code> is being built up as each new word results in a new table
entry (highlighted with a green background), and a corresponding change record is sent to
the downstream <code>KStream</code>.
+</p>
+<p>
+When the second text line “hello kafka streams” is processed, we observe, for the first
time, that existing entries in the <code>KTable</code> are being updated (here:
for the words “kafka” and for “streams”). And again, change records are being sent
to the output topic.
+</p>
+<p>
+And so on (we skip the illustration of how the third line is being processed). This explains
why the output topic has the contents we showed above, because it contains the full record
of changes.
+</p>
+
+<p>
+Looking beyond the scope of this concrete example, what Kafka Streams is doing here is to
leverage the duality between a table and a changelog stream (here: table = the KTable, changelog
stream = the downstream KStream): you can publish every change of the table to a stream, and
if you consume the entire changelog stream from beginning to end, you can reconstruct the
contents of the table.
+</p>
+
+<p>
+Now you can write more input messages to the <b>streams-file-input</b> topic
and observe additional messages added
+to <b>streams-wordcount-output</b> topic, reflecting updated word counts (e.g.,
using the console producer and the
+console consumer, as described above).
+</p>
+
+<p>You can stop the console consumer via <b>Ctrl-C</b>.</p>
+
+</script>
+
+<div class="p-quickstart"></div>


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