trifecta

by ldaniels528

ldaniels528 / trifecta

Trifecta is a web-based and CLI tool that simplifies inspecting Kafka messages and Zookeeper data. A...

215 Stars 52 Forks Last release: about 4 years ago (v0.21.3) Apache License 2.0 1.7K Commits 36 Releases

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Trifecta

Trifecta is a web-based and Command Line Interface (CLI) tool that enables users to quickly and easily inspect, verify and even query Kafka messages. In addition, Trifecta offers data import/export functions for transferring data between Kafka topics and many other Big Data Systems (including Cassandra, ElasticSearch, MongoDB and others).

Table of Contents

Motivations

The motivations behind creating Trifecta are simple; testing, verifying and managing Kafka topics and Zookeeper key-value pairs is an arduous task. The goal of this project is to ease the pain of developing applications that make use of Kafka and ZooKeeper via a console-based tool using simple Unix-like (or SQL-like) commands.

Features

Development

Build Requirements

External Dependencies

In order to build from the source, you'll need to download the above dependencies and issue the following command for each of them:

$ sbt publish-local

Building the applications

Trifecta's build process produces two distinct applications, the command-line interface (trifectacli) and the Web-based user interface (trifectaui)

Building Trifecta CLI (Command-line interface)

$ sbt "project trifecta_cli" assembly

Building Trifecta UI (Typesafe Play application)

$ sbt "project trifecta_ui" dist

Running the tests

$ sbt clean test    

Configuring the application

On startup, Trifecta reads $HOME/.trifecta/config.properties (or creates the file if it doesn't exist). This file contains the configuration properties and connection strings for all supported systems.

# common properties
trifecta.common.autoSwitching = true
trifecta.common.columns = 25
trifecta.common.debugOn = false
trifecta.common.encoding = UTF-8

Kafka/Zookeeper properties

trifecta.zookeeper.host = localhost:2181

supports the setting of a path prefix for multi-tenant Zookeeper setups

#trifecta.zookeeper.kafka.root.path = /kafka

indicates whether Storm Partition Manager-style consumers should be read from Zookeeper

trifecta.kafka.consumers.storm = false

Cassandra properties

trifecta.cassandra.hosts = localhost

ElasticSearch properties

trifecta.elasticsearch.hosts = localhost:9200

MongoDB properties

trifecta.mongodb.hosts = localhost

Configuring Kafka Consumers

Trifecta currently supports 3 types of consumers: * Zookeeper Consumer Groups (Kafka 0.8.x) * Kafka-native Consumer Groups (Kafka 0.9.x) * Storm Partition Manager Consumers (Apache Storm-specific)

The most common type in use today are the Kafka-native consumers.

Kafka-native Consumer Groups

Kafka-native consumers require the consumer IDs that you want to monitor to be register via the trifecta.kafka.consumers.native property. Only registered consumer IDs (and their respective offsets will be visible).

    trifecta.kafka.consumers.native = dev,test,qa

Zookeeper Consumer Groups

Zookeeper-based consumers are enabled by default; however, they can be disabled (which will improve performance) by setting the trifecta.kafka.consumers.zookeeper property to false.

    trifecta.kafka.consumers.zookeeper = false

Apache Storm Partition Manager Consumer Groups

Storm Partition Manager consumers are disabled by default; however, they can be enabled (which will impact performance) by setting the trifecta.kafka.consumers.storm property be set to true.

    trifecta.kafka.consumers.storm = true

Run the application

To start the Trifecta REPL:

$ java -jar trifecta_cli_0.20.0.bin.jar

Optionally, you can execute Trifecta instructions (commands) right from the command line:

$ java -jar trifecta_cli_0.20.0.bin.jar kls -l

Downloads

Trifecta binaries are available for immediate download in the "releases" section.

What's New

v0.22.0

  • Reimplemented Publish and Query views (ported from v0.20.0)
  • Now offering simultaneous support for Kafka 0.8.x, 0.9.x and 0.10.x

v0.21.2

  • Added support for Kafka 0.10.0.0
  • Added support for AVDL
  • Updated to use MEANS.js 0.2.3.0 (Scalajs-Nodejs)

v0.20.0

  • Trifecta UI (CLI version)
    • Miscellaneous bug fixes
  • Trifecta UI (TypeSafe Play version)
    • Miscellaneous bug fixes

v0.19.2

  • Trifecta UI (TypeSafe version)
    • Fixed issue with out of memory errors while streaming messages

v0.19.1

  • Trifecta UI (CLI version)
    • Fixed issue with missing web resources

v0.19.0

  • Trifecta UI
    • Now a TypeSafe Play Application
    • Updated the user interface
    • Bug fixes

v0.18.1 to v0.18.20

  • Trifecta Core

    • Fixed issue with the application failing if the configuration file is not found
    • Upgraded to Kafka 0.8.2-beta
    • Kafka Query language (KQL) (formerly Big-Data Query Language/BDQL) has grammar simplification
    • Upgraded to Kafka 0.8.2.0
    • Added configuration key to support multi-tenant Zookeeper setups
    • Added support for Kafka ~~v0.8.2.0~~ v9.0.0 consumers
  • Trifecta UI

    • Added capability to navigate directly from a message (in the Inspect tab) to its decoder (in the Decoders tab)
    • Decoder tab user interface improvements
    • Observe tab user interface improvements
      • The Consumers section has been enhanced to display topic and consumer offset deltas
      • Redesigned the Replicas view to report under-replicated partitions.
      • The Topics section has been enhanced to display topic offset deltas
    • Query tab user interface improvements
      • Multiple queries can be executed concurrently
    • The embedded web server now supports asynchronous request/response flows
    • Added real-time message streaming capability to the Inspect tab
    • Swapped the Inspect and Observe modules
    • Added a new Brokers view to the Observe module
    • Reworked the Brokers view (Inspect module)
    • Fixed sort ordering of partitions in the Replicas view (Inspect module)
    • Fixed potential bug related to retrieving the list of available brokers
    • Now a TypeSafe Play Application w/updated the user interface

Trifecta UI

Trifecta offers a single-page web application (via Angular.js) with a REST service layer and web-socket support, which offers a comprehensive and powerful set of features for inspecting Kafka topic partitions and messages.

Starting Trifecta UI (Play web application)

To start the Play web application, issue the following from the command line:

    $ unzip trifecta_ui-0.20.0.zip
    $ cd trifecta_ui-0.20.0/bin
    $ ./trifecta_ui &

You'll see a few seconds of log messages, then a prompt indicating the web interface is ready for use.

    [warn] application - Logger configuration in conf files is deprecated and has no effect. Use a logback configuration file instead.
    [info] p.a.l.c.ActorSystemProvider - Starting application default Akka system: application
    [info] application - Application has started
    [info] play.api.Play$ - Application started (Prod)
    [info] p.c.s.NettyServer$ - Listening for HTTP on /0:0:0:0:0:0:0:0:9000

Next, open your browser and navigate to http://localhost:9000 (if you're running locally or the hostname/IP address of the remote host where the web application is running).

Configuring Trifecta UI

Additionally, Trifecta UI introduces a few new properties to the application configuration file (located in $HOME/.trifecta/config.properties). NOTE: The property values shown below are the default values.

# the embedded web server host/IP and port for client connections
trifecta.web.host = localhost
trifecta.web.port = 8888

the interval (in seconds) that changes to consumer offsets will be pushed to web-socket clients

trifecta.web.push.interval.consumer = 15

the interval (in seconds) that sampling messages will be pushed to web-socket clients

trifecta.web.push.interval.sampling = 2

the interval (in seconds) that changes to topics (new messages) will be pushed to web-socket clients

trifecta.web.push.interval.topic = 15

the number of actors to create for servicing requests

trifecta.web.actor.concurrency = 10

Configuring default Avro Decoders

Trifecta UI supports decoding Avro-encoded messages and displaying them in JSON format. To associate an Avro schema to a Kafka topic, place the schema file in a subdirectory with the same name as the topic. For example, if I wanted to associate an Avro file named

quotes.avsc
to the Kafka topic
shocktrade.quotes.avro
, I'd setup the following file structure:
$HOME/.trifecta/decoders/shocktrade.quotes.avro/quotes.avsc

The name of the actual schema file can be anything you'd like. Once the file has been placed in the appropriate location, restart Trifecta UI, and your messages will be displayed in JSON format.

Additionally, once a "default" decoder is configured for a Kafka topic, the CLI application can use them as well. For more details about using default decoders with the CLI application click here.

Inspecting Kafka Messages

Trifecta UI has powerful support for viewing Kafka messages, and when the messages are in either JSON or Avro format Trifecta displays them as human readable (read: pretty) JSON documents.

Replicas

Trifecta UI provides a comprehensive view of the current state of replication for each topic partition.

Queries

Trifecta UI also provides a way to execute queries against Avro-encoded topics using the Kafka Query Language (KQL). KQL is a SQL-like language with syntax as follows:

select  from  [with ]
[where ]
[limit ]

Consider the following example:

select symbol, exchange, lastTrade, open, close, high, low
from "shocktrade.quotes.avro"
where lastTrade <= 1 and volume >= 1,000,000
limit 25

The above query retrieves the

symbol
,
exchange
,
lastTrade
,
open
,
close
,
high
and
low
fields from messages within the Kafka topic
shocktrade.quotes.avro
(using the default decoder) filtering for only messages where the
lastTrade
is less than or equal to
1
, the
volume
is greater than or equal to
1,000,000
, and limiting the number of results to
25
.

Now consider a similar example, except here we'll specify a custom decoder file (

avro/quotes.avsc
):
select symbol, exchange, lastTrade, open, close, high, low
from "shocktrade.quotes.avro" with "avro:file:avro/quotes.avsc"
where lastTrade <= 1 and volume >= 1,000,000
limit 25

For more detailed information about KQL queries, click here.

Trifecta CLI

Trifecta CLI (Command Line Interface) is a REPL tool that simplifies inspecting Kafka messages, Zookeeper data, and optionally Elastic Search documents and MongoDB documents via simple UNIX-like commands (or SQL-like queries).

Core Module

Trifecta exposes its commands through modules. At any time to see which modules are available one could issue the

modules
command.
core:/home/ldaniels> modules
+ ---------------------------------------------------------------------------------------------------------- +
| name           className                                                                  loaded  active   |
+ ---------------------------------------------------------------------------------------------------------- +
| cassandra      com.github.ldaniels528.trifecta.modules.cassandra.CassandraModule          true    false    |
| core           com.github.ldaniels528.trifecta.modules.core.CoreModule                    true    true     |
| elasticSearch  com.github.ldaniels528.trifecta.modules.elasticsearch.ElasticSearchModule  true    false    |
| etl            com.github.ldaniels528.trifecta.modules.etl.ETLModule                      true    false    |
| kafka          com.github.ldaniels528.trifecta.modules.kafka.KafkaModule                  true    false    |
| mongodb        com.github.ldaniels528.trifecta.modules.mongodb.MongoModule                true    false    |
| zookeeper      com.github.ldaniels528.trifecta.modules.zookeeper.ZookeeperModule          true    false    |
+ ---------------------------------------------------------------------------------------------------------- +

To execute local system commands, enclose the command you'd like to execute using the back-ticks (`) symbol:

core:/home/ldaniels> `netstat -ptln`

To see all available commands, use the

help
command (
?
is a shortcut):
core:/home/ldaniels> ?
+ ---------------------------------------------------------------------------------------------------------------------- +
| command     module     description                                                                                     |
+ ---------------------------------------------------------------------------------------------------------------------- +
| !           core       Executes a previously issued command                                                            |
| $           core       Executes a local system command                                                                 |
| ?           core       Provides the list of available commands                                                         |
| autoswitch  core       Automatically switches to the module of the most recently executed command                      |
.                                                                                                                        .
.                                                                                                                        .
.                                                                                                                        .                                          
| ztree       zookeeper  Retrieves Zookeeper directory structure                                                         |
+ ---------------------------------------------------------------------------------------------------------------------- +

To see the syntax/usage of a command, use the

syntax
command:
core:/home/ldaniels> syntax kget
Description: Retrieves the message at the specified offset for a given topic partition
Usage: kget [-o outputSource] [-d YYYY-MM-DDTHH:MM:SS] [-a avroSchema] [topic] [partition] [offset]

Kafka Module

To view all of the Kafka commands, use the

-m
switch and the module name
kafka:/> ? -m kafka
+ ------------------------------------------------------------------------------------------------------------------- +
| command     module  description                                                                                     |
+ ------------------------------------------------------------------------------------------------------------------- +
| kbrokers    kafka   Returns a list of the brokers from ZooKeeper                                                    |
| kcommit     kafka   Commits the offset for a given topic and group                                                  |
| kconsumers  kafka   Returns a list of the consumers from ZooKeeper                                                  |
| kcount      kafka   Counts the messages matching a given condition                                                  |
| kcursor     kafka   Displays the message cursor(s)                                                                  |
| kfetch      kafka   Retrieves the offset for a given topic and group                                                |
| kfetchsize  kafka   Retrieves or sets the default fetch size for all Kafka queries                                  |
| kfind       kafka   Finds messages matching a given condition and exports them to a topic                           |
| kfindone    kafka   Returns the first occurrence of a message matching a given condition                            |
| kfirst      kafka   Returns the first message for a given topic                                                     |
| kget        kafka   Retrieves the message at the specified offset for a given topic partition                       |
| kgetkey     kafka   Retrieves the key of the message at the specified offset for a given topic partition            |
| kgetminmax  kafka   Retrieves the smallest and largest message sizes for a range of offsets for a given partition   |
| kgetsize    kafka   Retrieves the size of the message at the specified offset for a given topic partition           |
| kinbound    kafka   Retrieves a list of topics with new messages (since last query)                                 |
| klast       kafka   Returns the last message for a given topic                                                      |
| kls         kafka   Lists all existing topics                                                                       |
| knext       kafka   Attempts to retrieve the next message                                                           |
| kprev       kafka   Attempts to retrieve the message at the previous offset                                         |
| kreset      kafka   Sets a consumer group ID to zero for all partitions                                             |
| kstats      kafka   Returns the partition details for a given topic                                                 |
| kswitch     kafka   Switches the currently active topic cursor                                                      |
+ ------------------------------------------------------------------------------------------------------------------- +

Kafka Brokers

To list the replica brokers that Zookeeper is aware of:

kafka:/> kbrokers
+ ---------------------------------------------------------- +
| jmx_port  timestamp                host    version  port   |
+ ---------------------------------------------------------- +
| 9999      2014-08-23 19:33:01 PDT  dev501  1        9093   |
| 9999      2014-08-23 18:41:07 PDT  dev501  1        9092   |
| 9999      2014-08-23 18:41:07 PDT  dev501  1        9091   |
| 9999      2014-08-23 20:05:17 PDT  dev502  1        9093   |
| 9999      2014-08-23 20:05:17 PDT  dev502  1        9092   |
| 9999      2014-08-23 20:05:17 PDT  dev502  1        9091   |
+ ---------------------------------------------------------- +

Kafka Topics

To list all of the Kafka topics that Zookeeper is aware of:

kafka:/> kls
+ ------------------------------------------------------ +
| topic                         partitions  replicated   |
+ ------------------------------------------------------ +
| Shocktrade.quotes.csv         5           100%         |
| shocktrade.quotes.avro        5           100%         |
| test.Shocktrade.quotes.avro   5           100%         |
| hft.Shocktrade.quotes.avro    5           100%         |
| test2.Shocktrade.quotes.avro  5           100%         |
| test1.Shocktrade.quotes.avro  5           100%         |
| test3.Shocktrade.quotes.avro  5           100%         |
+ ------------------------------------------------------ +

To see a subset of the topics (matches any topic that starts with the given search term):

kafka:/> kls shocktrade.quotes.avro
+ ------------------------------------------------ +
| topic                   partitions  replicated   |
+ ------------------------------------------------ +
| shocktrade.quotes.avro  5           100%         |
+ ------------------------------------------------ +

To see a detailed list including all partitions, use the

-l
flag:
kafka:/> kls -l shocktrade.quotes.avro
+ ------------------------------------------------------------------ +
| topic                   partition  leader       replicas  inSync   |
+ ------------------------------------------------------------------ +
| shocktrade.quotes.avro  0          dev501:9092  1         1        |
| shocktrade.quotes.avro  1          dev501:9093  1         1        |
| shocktrade.quotes.avro  2          dev502:9091  1         1        |
| shocktrade.quotes.avro  3          dev502:9092  1         1        |
| shocktrade.quotes.avro  4          dev502:9093  1         1        |
+ ------------------------------------------------------------------ +

To see the statistics for a specific topic, use the

kstats
command:
kafka:/> kstats Shocktrade.quotes.csv
+ --------------------------------------------------------------------------------- +
| topic                      partition  startOffset  endOffset  messagesAvailable   |
+ --------------------------------------------------------------------------------- +
| Shocktrade.quotes.csv      0          5945         10796      4851                |
| Shocktrade.quotes.csv      1          5160         10547      5387                |
| Shocktrade.quotes.csv      2          3974         8788       4814                |
| Shocktrade.quotes.csv      3          3453         7334       3881                |
| Shocktrade.quotes.csv      4          4364         8276       3912                |
+ --------------------------------------------------------------------------------- +

Kafka Navigable Cursor

The Kafka module offers the concept of a navigable cursor. Any command that references a specific message offset creates a pointer to that offset, called a navigable cursor. Once the cursor has been established, with a single command, you can navigate to the first, last, previous, or next message using the

kfirst
,
klast
,
kprev
and
knext
commands respectively. Consider the following examples:

To retrieve the first message of a topic partition:

kafka:/> kfirst Shocktrade.quotes.csv 0
[5945:000] 22.47.44.46.22.2c.31.30.2e.38.31.2c.22.39.2f.31.32.2f.32.30.31.34.22.2c.22 | "GDF",10.81,"9/12/2014"," |
[5945:025] 34.3a.30.30.70.6d.22.2c.4e.2f.41.2c.4e.2f.41.2c.2d.30.2e.31.30.2c.22.2d.30 | 4:00pm",N/A,N/A,-0.10,"-0 |
[5945:050] 2e.31.30.20.2d.20.2d.30.2e.39.32.25.22.2c.31.30.2e.39.31.2c.31.30.2e.39.31 | .10 - -0.92%",10.91,10.91 |
[5945:075] 2c.31.30.2e.38.31.2c.31.30.2e.39.31.2c.31.30.2e.38.30.2c.33.36.35.35.38.2c | ,10.81,10.91,10.80,36558, |
[5945:100] 4e.2f.41.2c.22.4e.2f.41.22                                                 | N/A,"N/A"                 |

The previous command resulted in the creation of a navigable cursor (notice below how our prompt has changed).

kafka:Shocktrade.quotes.csv/0:5945> _

Let's view the cursor:

kafka:Shocktrade.quotes.csv/0:5945> kcursor
+ ------------------------------------------------------------------- +
| topic                      partition  offset  nextOffset  decoder   |
+ ------------------------------------------------------------------- +
| Shocktrade.quotes.csv      0          5945    5946                  |
+ ------------------------------------------------------------------- +

Let's view the next message for this topic partition:

kafka:Shocktrade.quotes.csv/0:5945> knext
[5946:000] 22.47.44.50.22.2c.31.38.2e.35.31.2c.22.39.2f.31.32.2f.32.30.31.34.22.2c.22 | "GDP",18.51,"9/12/2014"," |
[5946:025] 34.3a.30.31.70.6d.22.2c.4e.2f.41.2c.4e.2f.41.2c.2d.30.2e.38.39.2c.22.2d.30 | 4:01pm",N/A,N/A,-0.89,"-0 |
[5946:050] 2e.38.39.20.2d.20.2d.34.2e.35.39.25.22.2c.31.39.2e.34.30.2c.31.39.2e.32.37 | .89 - -4.59%",19.40,19.27 |
[5946:075] 2c.31.38.2e.35.31.2c.31.39.2e.33.32.2c.31.38.2e.33.30.2c.31.35.31.36.32.32 | ,18.51,19.32,18.30,151622 |
[5946:100] 30.2c.38.32.32.2e.33.4d.2c.22.4e.2f.41.22                                  | 0,822.3M,"N/A"            |                                                    | M,"N/A"                   |

Let's view the last message for this topic partition:

kafka:Shocktrade.quotes.csv/0:5945> klast
[10796:000] 22.4e.4f.53.50.46.22.2c.30.2e.30.30.2c.22.4e.2f.41.22.2c.22.4e.2f.41.22.2c | "NOSPF",0.00,"N/A","N/A", |
[10796:025] 4e.2f.41.2c.4e.2f.41.2c.4e.2f.41.2c.22.4e.2f.41.20.2d.20.4e.2f.41.22.2c.4e | N/A,N/A,N/A,"N/A - N/A",N |
[10796:050] 2f.41.2c.4e.2f.41.2c.30.2e.30.30.2c.4e.2f.41.2c.4e.2f.41.2c.4e.2f.41.2c.4e | /A,N/A,0.00,N/A,N/A,N/A,N |
[10796:075] 2f.41.2c.22.54.69.63.6b.65.72.20.73.79.6d.62.6f.6c.20.68.61.73.20.63.68.61 | /A,"Ticker symbol has cha |
[10796:100] 6e.67.65.64.20.74.6f.3a.20.3c.61.20.68.72.65.66.3d.22.2f.71.3f.73.3d.4e.4f | nged to: NOSPF"           |                                          | ,N/A,"N/A"                |

Notice above we didn't have to specify the topic or partition because it's defined in our cursor. Let's view the cursor again:

kafka:Shocktrade.quotes.csv/0:10796> kcursor
+ ------------------------------------------------------------------- +
| topic                      partition  offset  nextOffset  decoder   |
+ ------------------------------------------------------------------- +
| Shocktrade.quotes.csv      0          10796   10797                 |
+ ------------------------------------------------------------------- +

Now, let's view the previous record:

kafka:Shocktrade.quotes.csv/0:10796> kprev
[10795:000] 22.4d.4c.50.4b.46.22.2c.30.2e.30.30.2c.22.4e.2f.41.22.2c.22.4e.2f.41.22.2c | "MLPKF",0.00,"N/A","N/A", |
[10795:025] 4e.2f.41.2c.4e.2f.41.2c.4e.2f.41.2c.22.4e.2f.41.20.2d.20.4e.2f.41.22.2c.4e | N/A,N/A,N/A,"N/A - N/A",N |
[10795:050] 2f.41.2c.4e.2f.41.2c.30.2e.30.30.2c.4e.2f.41.2c.4e.2f.41.2c.4e.2f.41.2c.4e | /A,N/A,0.00,N/A,N/A,N/A,N |
[10795:075] 2f.41.2c.22.54.69.63.6b.65.72.20.73.79.6d.62.6f.6c.20.68.61.73.20.63.68.61 | /A,"Ticker symbol has cha |
[10795:100] 6e.67.65.64.20.74.6f.3a.20.3c.61.20.68.72.65.66.3d.22.2f.71.3f.73.3d.4d.4c | nged to: MLPKF"           |                                               | N/A,"N/A"                 |

To retrieve the start and end offsets and number of messages available for a topic across any number of partitions:

kafka:Shocktrade.quotes.csv/0:10795> kstats
+ --------------------------------------------------------------------------------- +
| topic                      partition  startOffset  endOffset  messagesAvailable   |
+ --------------------------------------------------------------------------------- +
| Shocktrade.quotes.csv      0          5945         10796      4851                |
| Shocktrade.quotes.csv      1          5160         10547      5387                |
| Shocktrade.quotes.csv      2          3974         8788       4814                |
| Shocktrade.quotes.csv      3          3453         7334       3881                |
| Shocktrade.quotes.csv      4          4364         8276       3912                |
+ --------------------------------------------------------------------------------- +

NOTE: Above

kstats
is equivalent to both
kstats Shocktrade.quotes.csv
and
kstats Shocktrade.quotes.csv 0 4
. However, because of the cursor we previously established, those arguments could be omitted.

Kafka Consumer Groups

To see the current offsets for all consumer group IDs:

kafka:Shocktrade.quotes.csv/0:10795> kconsumers
+ ------------------------------------------------------------------------------------- +
| consumerId  topic                      partition  offset  topicOffset  messagesLeft   |
+ ------------------------------------------------------------------------------------- +
| dev         Shocktrade.quotes.csv      0          5555    10796        5241           |
| dev         Shocktrade.quotes.csv      1          0       10547        10547          |
| dev         Shocktrade.quotes.csv      2          0       8788         8788           |
| dev         Shocktrade.quotes.csv      3          0       7334         7334           |
| dev         Shocktrade.quotes.csv      4          0       8276         8276           |
+ ------------------------------------------------------------------------------------- +

Let's change the committed offset for the current topic/partition (the one to which our cursor is pointing) to 6000

kafka:Shocktrade.quotes.csv/0:10795> kcommit dev 6000

Let's re-examine the consumer group IDs:

kafka:Shocktrade.quotes.csv/0:10795> kconsumers
+ ------------------------------------------------------------------------------------- +
| consumerId  topic                      partition  offset  topicOffset  messagesLeft   |
+ ------------------------------------------------------------------------------------- +
| dev         Shocktrade.quotes.csv      0          6000    10796        4796           |
| dev         Shocktrade.quotes.csv      1          0       10547        10547          |
| dev         Shocktrade.quotes.csv      2          0       8788         8788           |
| dev         Shocktrade.quotes.csv      3          0       7334         7334           |
| dev         Shocktrade.quotes.csv      4          0       8276         8276           |
+ ------------------------------------------------------------------------------------- +

Notice that the committed offset, for consumer group dev, has been changed to 6000 for partition 0.

Finally, let's use the

kfetch
to retrieve just the offset for the consumer group ID:
kafka:Shocktrade.quotes.csv/0:10795> kfetch dev
6000

The Kafka Module also provides the capability for watching messages (in near real-time) as they become available. When you watch a topic, a watch cursor is created, which allows you to move forward through the topic as new messages appear. This differs from navigable cursors, which allow you to move back and forth through a topic at will. You can take advantage of this feature by using two built-in commands:

kwatch
and
kwatchnext
.

First,

kwatch
is used to create a connection to a consumer group:
kafka:Shocktrade.quotes.csv/0:10795> kwatch shocktrade.quotes.avro dev -a file:avro/quotes.avsc

Some output will likely follow as the connection (and watch cursor) are established. Next, if a message is already available, it will be returned immediately:

{
  "symbol":"MNLDF",
  "exchange":"OTHER OTC",
  "lastTrade":1.18,
  "tradeDate":null,
  "tradeTime":"1:50pm",
  "ask":null,
  "bid":null,
  "change":0.0,
  "changePct":0.0,
  "prevClose":1.18,
  "open":null,
  "close":1.18,
  "high":null,
  "low":null,
  "volume":0,
  "marketCap":null,
  "errorMessage":null
}

kafka:[w]shocktrade.quotes.avro/4:23070> _

To retrieve any subsequent messages, you use the

kwatchnext
command:
kafka:[w]shocktrade.quotes.avro/4:23070> kwatchnext

{ "symbol":"PXYN", "exchange":"OTHER OTC", "lastTrade":0.075, "tradeDate":null, "tradeTime":"3:57pm", "ask":null, "bid":null, "change":0.01, "changePct":15.38, "prevClose":0.065, "open":0.064, "close":0.075, "high":0.078, "low":0.064, "volume":1325779, "marketCap":2.45E7, "errorMessage":null }

kafka:[w]shocktrade.quotes.avro/4:23071> _

Did you notice the "[w]" you see in the prompt? This indicates a watch cursor is active. However, because each call to

kwatchnext
will also update the navigable cursor, you are free to also use the bi-directional navigation commands
knext
and
kprev
(as well as
kfirst
and
klast
). Be aware though, that changes to the navigable cursor do not affect the watch cursor. Thus after a subsequent use of
kwatchnext
, the navigable cursor will be overwritten.

Kafka Inbound Traffic

To retrieve the list of topics with new messages (since your last query):

kafka:/> kinbound
+ ----------------------------------------------------------------------------------------------------------- +
| topic                        partition  startOffset  endOffset  change  msgsPerSec  lastCheckTime           |
+ ----------------------------------------------------------------------------------------------------------- +
| Shocktrade.quotes.avro       3          0            657        65      16.3        09/13/14 06:37:03 PDT   |
| Shocktrade.quotes.avro       0          0            650        64      16.0        09/13/14 06:37:03 PDT   |
| Shocktrade.quotes.avro       1          0            618        56      14.0        09/13/14 06:37:03 PDT   |
| Shocktrade.quotes.avro       2          0            618        49      12.3        09/13/14 06:37:03 PDT   |
| Shocktrade.quotes.avro       4          0            584        40      10.0        09/13/14 06:37:03 PDT   |
+ ----------------------------------------------------------------------------------------------------------- +

Next, we wait a few moments and run the command again:

kafka:/> kinbound
+ ----------------------------------------------------------------------------------------------------------- +
| topic                        partition  startOffset  endOffset  change  msgsPerSec  lastCheckTime           |
+ ----------------------------------------------------------------------------------------------------------- +
| Shocktrade.quotes.avro       1          0            913        295     15.6        09/13/14 06:37:21 PDT   |
| Shocktrade.quotes.avro       3          0            952        295     15.6        09/13/14 06:37:21 PDT   |
| Shocktrade.quotes.avro       2          0            881        263     13.9        09/13/14 06:37:21 PDT   |
| Shocktrade.quotes.avro       4          0            846        262     13.8        09/13/14 06:37:21 PDT   |
| Shocktrade.quotes.avro       0          0            893        243     12.8        09/13/14 06:37:21 PDT   |
+ ----------------------------------------------------------------------------------------------------------- +

Kafka & Avro Integration

Trifecta supports Avro integration for Kafka. The next few examples make use of the following Avro schema:

{
    "type": "record",
    "name": "StockQuote",
    "namespace": "Shocktrade.avro",
    "fields":[
        { "name": "symbol", "type":"string", "doc":"stock symbol" },
        { "name": "lastTrade", "type":["null", "double"], "doc":"last sale price", "default":null },
        { "name": "tradeDate", "type":["null", "long"], "doc":"last sale date", "default":null },
        { "name": "tradeTime", "type":["null", "string"], "doc":"last sale time", "default":null },
        { "name": "ask", "type":["null", "double"], "doc":"ask price", "default":null },
        { "name": "bid", "type":["null", "double"], "doc":"bid price", "default":null },
        { "name": "change", "type":["null", "double"], "doc":"price change", "default":null },
        { "name": "changePct", "type":["null", "double"], "doc":"price change percent", "default":null },
        { "name": "prevClose", "type":["null", "double"], "doc":"previous close price", "default":null },
        { "name": "open", "type":["null", "double"], "doc":"open price", "default":null },
        { "name": "close", "type":["null", "double"], "doc":"close price", "default":null },
        { "name": "high", "type":["null", "double"], "doc":"day's high price", "default":null },
        { "name": "low", "type":["null", "double"], "doc":"day's low price", "default":null },
        { "name": "volume", "type":["null", "long"], "doc":"day's volume", "default":null },
        { "name": "marketCap", "type":["null", "double"], "doc":"market capitalization", "default":null },
        { "name": "errorMessage", "type":["null", "string"], "doc":"error message", "default":null }
    ],
    "doc": "A schema for stock quotes"
}

Let's retrieve the first message from topic Shocktrade.quotes.avro (partition 0) using an Avro schema as our optional message decoder:

kafka:/> kfirst Shocktrade.quotes.avro 0 -a file:avro/quotes.avsc

{ "symbol":"GES", "exchange":"NYSE", "lastTrade":21.75, "tradeDate":null, "tradeTime":"4:03pm", "ask":null, "bid":null, "change":-0.41, "changePct":-1.85, "prevClose":22.16, "open":21.95, "close":21.75, "high":22.16, "low":21.69, "volume":650298, "marketCap":1.853E9, "errorMessage":null }

Let's view the cursor:

kafka:shocktrade.quotes.avro/2:9728> kcursor
+ ---------------------------------------------------------------------------------- +
| topic                         partition  offset  nextOffset  decoder               |
+ ---------------------------------------------------------------------------------- +
| Shocktrade.quotes.avro        0          0       1           AvroDecoder(schema)   |
+ ---------------------------------------------------------------------------------- +

The

kfirst
,
klast
,
kprev
and
knext
commands also work with the Avro integration:
kafka:shocktrade.quotes.avro/2:9728> knext

{ "symbol":"GEO", "exchange":"NYSE", "lastTrade":41.4, "tradeDate":null, "tradeTime":"4:03pm", "ask":null, "bid":null, "change":0.76, "changePct":1.87, "prevClose":40.64, "open":40.55, "close":41.4, "high":41.49, "low":40.01, "volume":896757, "marketCap":2.973E9, "errorMessage":null }

Since Avro is based on JSON, we can also express the same data in Avro compatible JSON format:

kafka:shocktrade.quotes.avro/2:9728> kget -f avro_json

{ "symbol":"GEO", "exchange":{ "string":"NYSE" }, "lastTrade":{ "double":41.4 }, "tradeDate":null, "tradeTime":{ "string":"4:03pm" }, "ask":null, "bid":null, "change":{ "double":0.76 }, "changePct":{ "double":1.87 }, "prevClose":{ "double":40.64 }, "open":{ "double":40.55 }, "close":{ "double":41.4 }, "high":{ "double":41.49 }, "low":{ "double":40.01 }, "volume":{ "long":896757 }, "marketCap":{ "double":2.973E9 }, "errorMessage":null }

Default Avro Decoders

You can also associate a Kafka topic to a "default" Avro schema. To associate an Avro schema to the topic, place the schema file in a subdirectory with the same name as the topic. For example, if I wanted to associate an Avro file named

quotes.avsc
to the Kafka topic
shocktrade.quotes.avro
, I'd setup the following file structure:
$HOME/.trifecta/decoders/Shocktrade.quotes.avro/quotes.avsc

The name of the actual schema file can be anything you'd like. Once the file has been placed in the appropriate location, restart Trifecta, and default decoder will be ready for use.

In our last example, we did the following:

kafka:/> kfirst Shocktrade.quotes.avro 0 -a file:avro/quotes.avsc

With a default decoder, we can do this instead:

kafka:/> kfirst Shocktrade.quotes.avro 0 -a default

Kafka Search by Key

You can view the key for any message by using the

kgetkey
command. Let's start by retrieving the last available message for a topic/partition.
kafka:/> klast Shocktrade.quotes.csv 0
[10796:000] 22.4e.4f.53.50.46.22.2c.30.2e.30.30.2c.22.4e.2f.41.22.2c.22.4e.2f.41.22.2c | "NOSPF",0.00,"N/A","N/A", |
[10796:025] 4e.2f.41.2c.4e.2f.41.2c.4e.2f.41.2c.22.4e.2f.41.20.2d.20.4e.2f.41.22.2c.4e | N/A,N/A,N/A,"N/A - N/A",N |
[10796:050] 2f.41.2c.4e.2f.41.2c.30.2e.30.30.2c.4e.2f.41.2c.4e.2f.41.2c.4e.2f.41.2c.4e | /A,N/A,0.00,N/A,N/A,N/A,N |
[10796:075] 2f.41.2c.22.54.69.63.6b.65.72.20.73.79.6d.62.6f.6c.20.68.61.73.20.63.68.61 | /A,"Ticker symbol has cha |
[10796:100] 6e.67.65.64.20.74.6f.3a.20.3c.61.20.68.72.65.66.3d.22.2f.71.3f.73.3d.4e.4f | nged to: NOSPF"           |

Now let's view the key for message using the

kgetkey
command:
kafka:Shocktrade.quotes.csv/0:10796> kgetkey
[000] 31.34.31.30.35.36.33.37.31.34.34.36.35                                     | 1410563714465             |

To ensure you'll notice the change in the cursor's position, let's reposition the cursor to the beginning of the topic/partition:

kafka:Shocktrade.quotes.csv/0:10796> kfirst
[5945:000] 22.47.44.46.22.2c.31.30.2e.38.31.2c.22.39.2f.31.32.2f.32.30.31.34.22.2c.22 | "GDF",10.81,"9/12/2014"," |
[5945:025] 34.3a.30.30.70.6d.22.2c.4e.2f.41.2c.4e.2f.41.2c.2d.30.2e.31.30.2c.22.2d.30 | 4:00pm",N/A,N/A,-0.10,"-0 |
[5945:050] 2e.31.30.20.2d.20.2d.30.2e.39.32.25.22.2c.31.30.2e.39.31.2c.31.30.2e.39.31 | .10 - -0.92%",10.91,10.91 |
[5945:075] 2c.31.30.2e.38.31.2c.31.30.2e.39.31.2c.31.30.2e.38.30.2c.33.36.35.35.38.2c | ,10.81,10.91,10.80,36558, |
[5945:100] 4e.2f.41.2c.22.4e.2f.41.22                                                 | N/A,"N/A"                 |

kafka:Shocktrade.quotes.csv/0:5945> _

Next, using the

kfindone
command let's search for the last message by key (using hexadecimal dot-notation):
kafka:Shocktrade.quotes.csv/0:5945> kfindone key is 31.34.31.30.35.36.33.37.31.34.34.36.35
Task is now running in the background (use 'jobs' to view)

You may have received the message Task is now running in the background (use 'jobs' to view). This happens whenever a query is executed that requires more than 5 seconds to complete. You may use the

jobs
command to check the status of a background task; however, either way, after a few moments the results should appear on the console:
kafka:Shocktrade.quotes.csv/0:5945> Job #1006 completed
[10796:000] 22.4e.4f.53.50.46.22.2c.30.2e.30.30.2c.22.4e.2f.41.22.2c.22.4e.2f.41.22.2c | "NOSPF",0.00,"N/A","N/A", |
[10796:025] 4e.2f.41.2c.4e.2f.41.2c.4e.2f.41.2c.22.4e.2f.41.20.2d.20.4e.2f.41.22.2c.4e | N/A,N/A,N/A,"N/A - N/A",N |
[10796:050] 2f.41.2c.4e.2f.41.2c.30.2e.30.30.2c.4e.2f.41.2c.4e.2f.41.2c.4e.2f.41.2c.4e | /A,N/A,0.00,N/A,N/A,N/A,N |
[10796:075] 2f.41.2c.22.54.69.63.6b.65.72.20.73.79.6d.62.6f.6c.20.68.61.73.20.63.68.61 | /A,"Ticker symbol has cha |
[10796:100] 6e.67.65.64.20.74.6f.3a.20.3c.61.20.68.72.65.66.3d.22.2f.71.3f.73.3d.4e.4f | nged to: NOSPF"           |

The result is the last message of the topic. Notice that our cursor has changed reflecting the move from offset 5945 to 10796.

kafka:Shocktrade.quotes.csv/0:10796> _

Kafka Advanced Search

Building on the Avro Integration, Trifecta offers the ability to execute queries against structured data.

Suppose you want to know how many messages contain a volume greater than 1,000,000, you could issue the

kcount
command:
kafka:Shocktrade.quotes.avro/0:1> kcount volume > 1000000
1350

The response was 1350, meaning there are 1350 messages containing a volume greater than 1,000,000.

Suppose you want to find a message for Apple (ticker: "AAPL"), you could issue the

kfindone
command:
kafka:Shocktrade.quotes.avro/0:1> kfindone symbol == "AAPL"

{ "symbol":"AAPL", "exchange":"NASDAQNM", "lastTrade":109.01, "tradeDate":null, "tradeTime":"4:00pm", "ask":109.03, "bid":109.01, "change":0.31, "changePct":0.29, "prevClose":108.7, "open":108.72, "close":109.01, "high":109.32, "low":108.55, "volume":33691536, "marketCap":6.393E11, "errorMessage":null }

You can also specify complex queries by combining multiple expressions with the

and
keyword:
kafka:Shocktrade.quotes.avro/1234:4> kfindone lastTrade < 1 and volume > 1000000 -a file:avro/quotes.avsc

{ "symbol":"MDTV", "exchange":"OTHER OTC", "lastTrade":0.022, "tradeDate":null, "tradeTime":"3:27pm", "ask":null, "bid":null, "change":0.0099, "changePct":81.82, "prevClose":0.0121, "open":0.015, "close":0.022, "high":0.027, "low":0.015, "volume":1060005, "marketCap":125000.0, "errorMessage":null }

Now suppose you want to copy the messages having high volume (1,000,000 or more) to another topic:

kafka:Shocktrade.quotes.avro/3300:3> kfind volume >= 1000000 -o topic:hft.Shocktrade.quotes.avro

Finally, let's look at the results:

kafka:Shocktrade.quotes.avro/3:0> kstats hft.Shocktrade.quotes.avro
+ ---------------------------------------------------------------------------------- +
| topic                       partition  startOffset  endOffset  messagesAvailable   |
+ ---------------------------------------------------------------------------------- +
| hft.Shocktrade.quotes.avro  0          0            283        283                 |
| hft.Shocktrade.quotes.avro  1          0            287        287                 |
| hft.Shocktrade.quotes.avro  2          0            258        258                 |
| hft.Shocktrade.quotes.avro  3          0            245        245                 |
| hft.Shocktrade.quotes.avro  4          0            272        272                 |
+ ---------------------------------------------------------------------------------- +

Let's see how these statistics compares to the original:

kafka:Shocktrade.quotes.avro/3:0> kstats Shocktrade.quotes.avro
+ ----------------------------------------------------------------------------------- +
| topic                        partition  startOffset  endOffset  messagesAvailable   |
+ ----------------------------------------------------------------------------------- +
| Shocktrade.quotes.avro       0          0            4539       4539                |
| Shocktrade.quotes.avro       1          0            4713       4713                |
| Shocktrade.quotes.avro       2          0            4500       4500                |
| Shocktrade.quotes.avro       3          0            4670       4670                |
| Shocktrade.quotes.avro       4          0            4431       4431                |
+ ----------------------------------------------------------------------------------- +

Searching By Query

Trifecta provides the ability to perform SQL-like queries against Avro-encoded Kafka topics (Read more about Trifecta's Kafka-Avro integration here). The syntax is very similar to SQL except for a few minor differences. Here's the basic syntax:

select 
from  with 
where 
limit 

Where decoder may be: * json * avro:file:path

Consider the following example:

kafka:shocktrade.quotes.avro/0:32050> select symbol, exchange, open, close, high, low
                                      from shocktrade.quotes.avro
                                      with "avro:file:avro/quotes.avsc"
                                      where symbol == "AAPL"

NOTE: Because we didn't specify a limit for the number of results that could be returned, the default value (25) is used.

As with most potentially long-running statements in Trifecta, if the query takes longer than a few seconds to complete, it will be executed in the background.

kafka:shocktrade.quotes.avro/0:32050> Job #607 completed (use 'jobs -v 607' to view results)
+ --------------------------------------------------------------------- +
| partition  offset  symbol  exchange  open    close   high    low      |
+ --------------------------------------------------------------------- +
| 0          32946   AAPL    NASDAQNM  108.72  109.01  109.32  108.55   |
+ --------------------------------------------------------------------- +

NOTE: Although the

partition
and
offset
fields weren't specified in the query, they are always included in the query results.

Let's look at another example:

kafka:shocktrade.quotes.avro/0:32050> select symbol, exchange, lastTrade, open, close, high, low
                                      from shocktrade.quotes.avro
                                      with "avro:file:avro/quotes.avsc"
                                      where lastTrade <= 1 and volume >= 1,000,000
                                      limit 25

Task is now running in the background (use 'jobs' to view) kafka:shocktrade.quotes.avro/0:32050> Job #873 completed (use 'jobs -v 873' to view results)

  • --------------------------------------------------------------------------------- + | partition offset symbol exchange lastTrade open close high low |
  • --------------------------------------------------------------------------------- + | 3 34047 NIHDQ OTHER OTC 0.0509 0.0452 0.0509 0.0549 0.0452 | | 3 33853 IMRS NASDAQNM 0.2768 0.25 0.2768 0.28 0.2138 | | 3 33818 VTMB OTHER OTC 0.0014 0.0013 0.0014 0.0014 0.0012 | | 3 33780 MLHC OTHER OTC 4.0E-4 5.0E-4 4.0E-4 5.0E-4 3.0E-4 | | 3 33709 ECDC OTHER OTC 1.0E-4 1.0E-4 1.0E-4 1.0E-4 1.0E-4 | | 3 33640 PWDY OTC BB 0.0037 0.0032 0.0037 0.0043 0.003 | | 3 33534 BPZ NYSE 0.9599 1.02 0.9599 1.0201 0.92 | | 3 33520 TAGG OTHER OTC 2.0E-4 1.0E-4 2.0E-4 2.0E-4 1.0E-4 | | 3 33515 MDMN OTHER OTC 0.055 0.051 0.055 0.059 0.051 | | 3 33469 MCET OTHER OTC 5.0E-4 5.0E-4 5.0E-4 5.0E-4 5.0E-4 | | 3 33460 GGSM OTHER OTC 3.0E-4 3.0E-4 3.0E-4 4.0E-4 3.0E-4 | | 3 33404 TDCP OTHER OTC 0.0041 0.0041 0.0041 0.0041 0.0038 | | 3 33337 GSS AMEX 0.305 0.27 0.305 0.31 0.266 | | 3 33254 MDTV OTHER OTC 0.022 0.015 0.022 0.027 0.015 | | 2 33246 AMRN NGM 0.9 0.9128 0.9 0.93 0.88 | | 2 33110 TRTC OTHER OTC 0.38 0.3827 0.38 0.405 0.373 | | 2 33068 AEMD OTHER OTC 0.2419 0.26 0.2419 0.2625 0.23 | | 2 33060 ZBB AMEX 0.6101 0.65 0.6101 0.65 0.55 | | 2 33058 TUNG OTHER OTC 0.0019 0.0021 0.0019 0.0021 0.0016 | | 2 33011 DKAM OTHER OTC 1.0E-4 2.0E-4 1.0E-4 2.0E-4 1.0E-4 | | 2 32984 ADMD OTHER OTC 0.001 0.001 0.001 0.0011 9.0E-4 | | 2 32905 GLDG OTHER OTC 2.0E-4 2.0E-4 2.0E-4 2.0E-4 2.0E-4 | | 2 32808 RBY AMEX 0.9349 0.821 0.9349 0.94 0.821 | | 2 32751 PZG AMEX 0.708 0.6 0.708 0.73 0.5834 | | 2 32731 PAL AMEX 0.15 0.15 0.15 0.1555 0.145 |
  • --------------------------------------------------------------------------------- +

Zookeeper Module

Zookeeper: Navigating directories and keys

To view the Zookeeper keys at the current hierarchy level:

[email protected]:2181:/> zls
consumers
storm
controller_epoch
admin
controller
brokers

To change the current Zookeeper hierarchy level:

zookeeper:localhost:2181:/> zcd brokers
/brokers

Now view the keys at this level:

zookeeper:localhost:2181:/brokers> zls
topics
ids

Let's look at the entire Zookeeper hierarchy recursively from our current path:

zookeeper:localhost:2181/brokers> ztree
/brokers
/brokers/topics
/brokers/topics/shocktrade.quotes.avro
/brokers/topics/shocktrade.quotes.avro/partitions
/brokers/topics/shocktrade.quotes.avro/partitions/0
/brokers/topics/shocktrade.quotes.avro/partitions/0/state
/brokers/topics/shocktrade.quotes.avro/partitions/1
/brokers/topics/shocktrade.quotes.avro/partitions/1/state
/brokers/topics/shocktrade.quotes.avro/partitions/2
/brokers/topics/shocktrade.quotes.avro/partitions/2/state
/brokers/topics/shocktrade.quotes.avro/partitions/3
/brokers/topics/shocktrade.quotes.avro/partitions/3/state
/brokers/topics/shocktrade.quotes.avro/partitions/4
/brokers/topics/shocktrade.quotes.avro/partitions/4/state
/brokers/ids
/brokers/ids/3
/brokers/ids/2
/brokers/ids/1
/brokers/ids/6
/brokers/ids/5
/brokers/ids/4

Zookeeper: Getting and setting key-value pairs

Let's view the contents of one of the keys:

zookeeper:localhost:2181/brokers> zget topics/Shocktrade.quotes.csv/partitions/4/state
[00] 7b.22.63.6f.6e.74.72.6f.6c.6c.65.72.5f.65.70.6f.63.68.22.3a.31.2c.22.6c.65 | {"controller_epoch":1,"le
[25] 61.64.65.72.22.3a.35.2c.22.76.65.72.73.69.6f.6e.22.3a.31.2c.22.6c.65.61.64 | ader":5,"version":1,"lead
[50] 65.72.5f.65.70.6f.63.68.22.3a.30.2c.22.69.73.72.22.3a.5b.35.5d.7d          | er_epoch":0,"isr":[5]}

Since we now know the contents of the key is text-based (JSON in this case), let's look at the JSON value using the format flag (

-f json
) to transform the JSON in a nice human-readable format.
zookeeper:localhost:2181/brokers> zget topics/Shocktrade.quotes.csv/partitions/4/state -f json
{
  "controller_epoch": 3,
  "leader": 6,
  "version": 1,
  "leader_epoch": 2,
  "isr": [6]
}

Next, let's set a key-value pair in Zookeeper:

zookeeper:localhost:2181/> zput /test/message "Hello World"

To retrieve the value we've just set, we can use the

zget
command again:
zookeeper:localhost:2181/> zget /test/message
[00] 48.65.6c.6c.6f.20.57.6f.72.6c.64                                           | Hello World

The

zput
command also allows us to set other types of values besides strings. The following example demonstrates setting a binary array literal using hexadecimal dot-notation.
zookeeper:localhost:2181/> zput /test/data de.ad.be.ef.ca.fe.ba.be

The default value types for integer and decimal values are

long
and
double
respectively. However, you can also explicitly set the value type using the type flag (
-t
).
zookeeper:localhost:2181/> zput /test/data2 123.45 -t float

The valid value types are:

  • bytes
  • char
  • double
  • float
  • integer
  • json
  • long
  • short
  • string

To verify that all of the key-value pairs were inserted we use the

zls
command again:
zookeeper:localhost:2181/> zls /test
data
message
data2

To view all of the Zookeeper commands, use the

-m
switch and the module name (
zookeeper
in this case):
zookeeper:localhost:2181/> ? -m zookeeper
+ ----------------------------------------------------------------------------------------- +
| command     module     description                                                        |
+ ----------------------------------------------------------------------------------------- +
| zcd         zookeeper  Changes the current path/directory in ZooKeeper                    |
| zexists     zookeeper  Verifies the existence of a ZooKeeper key                          |
| zget        zookeeper  Retrieves the contents of a specific Zookeeper key                 |
| zls         zookeeper  Retrieves the child nodes for a key from ZooKeeper                 |
| zmk         zookeeper  Creates a new ZooKeeper sub-directory (key)                        |
| zput        zookeeper  Sets a key-value pair in ZooKeeper                                 |
| zreconnect  zookeeper  Re-establishes the connection to Zookeeper                         |
| zrm         zookeeper  Removes a key-value from ZooKeeper (DESTRUCTIVE)                   |
| zruok       zookeeper  Checks the status of a Zookeeper instance (requires netcat)        |
| zstats      zookeeper  Returns the statistics of a Zookeeper instance (requires netcat)   |
| ztree       zookeeper  Retrieves Zookeeper directory structure                            |
+ ----------------------------------------------------------------------------------------- +

Cassandra Module

Trifecta has basic built-in support for querying and inspecting the state of Apache Cassandra clusters. To establish a connection to a Cassandra cluster, use the

cqconnect
command:
core:/home/ldaniels> cqconnect dev528

From this point, you need to select a keyspace to execute queries against. To do this, use the

keyspace
command:
cql:cluster1:/> keyspace shocktrade

Now that a keyspace has been selected, you can execute queries against the column families (tables) found within the keyspace.

cql:cluster1:shocktrade> cql "select name, symbol, exchange, lastTrade, volume from stocks where symbol = 'AAPL' limit 5"
+ --------------------------------------------------- +
| name        lasttrade  exchange  symbol  volume     |
+ --------------------------------------------------- +
| Apple Inc.  557.36     NASDAQ    AAPL    14067517   |
+ --------------------------------------------------- +

You can view basic cluster information by issuing the

clusterinfo
command:
cql:cluster1:shocktrade> clusterinfo
+ -------------------------------------------------------------------- +
| name                   value                                         |
+ -------------------------------------------------------------------- +
| Cluster Name           cluster1                                      |
| Partitioner            org.apache.cassandra.dht.Murmur3Partitioner   |
| Consistency Level      ONE                                           |
| Fetch Size             5000                                          |
| JMX Reporting Enabled  true                                          |
+ -------------------------------------------------------------------- +    

You can describe keyspaces and tables with the

describe
command:
cql:cluster1:shocktrade> describe keyspace shocktrade 

CREATE KEYSPACE shocktrade WITH REPLICATION = { 'class' : 'org.apache.cassandra.locator.SimpleStrategy', 'replication_factor': '3' } AND DURABLE_WRITES = true;

You can retrieve the list of all the commands that Cassandra Module offers with the help (?) command.

? -m cassandra

Elastic Search Module

To establish a connection to a local/remote Elastic Search peer, use the

econnect
command:
 core:/home/ldaniels> econnect dev501:9200    
 + ----------------------------------- +
 | name                   value        |
 + ----------------------------------- +
 | Cluster Name           ShockTrade   |
 | Status                 green        |
 | Timed Out              false        |
 | Number of Nodes        5            |
 | Number of Data Nodes   5            |
 | Active Shards          10           |
 | Active Primary Shards  5            |
 | Initializing Shards    0            |
 | Relocating Shards      0            |
 | Unassigned Shards      0            |
 + ----------------------------------- +

Once connected, the server statistics above will be returned.

To create a document, use the

eput
command:
elasticSearch:localhost:9200/> eput /quotes/quote/AMD { "symbol":"AMD", "lastSale":3.33 }

  • --------------------------------------- + | created _index _type _id _version |
  • --------------------------------------- + | true quotes quote AMD 3 |
  • --------------------------------------- +

To retrieve the document we've just created, use the

eget
command:
elasticSearch:localhost:9200/quotes/quote/AMD> eget /quotes/quote/AMD  
{
"symbol":"AMD",
"lastSale":3.55
}    

Elastic Search: Avro to JSON

Now let's do something slightly more advanced. Let's use Trifecta's powerful search and copy features to copy a message from a Kafka Topic to create (or update) an Elastic Search document. NOTE: Some setup steps have been omitted for brevity. See Kafka Advanced Search for full details.

elasticSearch:localhost:9200/quotes/quote/AMD> kfindone symbol == "AAPL" -o es:/quotes/quote/AAPL

{ "symbol":"AAPL", "exchange":"NASDAQNM", "lastTrade":109.01, "tradeDate":null, "tradeTime":"4:00pm", "ask":109.03, "bid":109.01, "change":0.31, "changePct":0.29, "prevClose":108.7, "open":108.72, "close":109.01, "high":109.32, "low":108.55, "volume":33691536, "marketCap":6.393E11, "errorMessage":null }

Finally, let's view the document we've created:

 kafka:shocktrade.quotes.avro/4:5429> eget /quotes/quote/AAPL

{ "symbol":"AAPL", "exchange":"NASDAQNM", "lastTrade":109.01, "tradeDate":null, "tradeTime":"4:00pm", "ask":109.03, "bid":109.01, "change":0.31, "changePct":0.29, "prevClose":108.7, "open":108.72, "close":109.01, "high":109.32, "low":108.55, "volume":33691536, "marketCap":6.393E11, "errorMessage":null }

Please note we could also use any of the Kafka message retrieve commands (

kget
,
kfirst
,
knext
,
kprev
and
klast
) to copy a Kafka message as an Elastic Search document. See the following example:
kafka:shocktrade.quotes.avro/4:5429> kget -o es:/quotes/quote/AAPL

Sometimes we need to copy a set of messages, and copying them one-by-one can be tedious and time-consuming; however, there is a command for just this sort of use-case. The

copy
command can be used to copy messages from any Avro- or JSON-capable input source (e.g. Kafka or Elastic Search) to any Avro- or JSON-capable output source. Consider the following example. Here we're copying messages from a Kafka topic to an Elastic Search index using the symbol field of the message as the ID for the Elastic Search document.
es:localhost:9200/> copy -i topic:shocktrade.quotes.avro -o es:/quotes/quote/$symbol -a file:avro/quotes.avsc

Once the operation has completed, the copy statistics are displayed:

es:localhost:9200/> Job #1845 completed (use 'jobs -v 1845' to view results)
+ -------------------------------------------------- +
| runTimeSecs  records  failures  recordsPerSecond   |
+ -------------------------------------------------- +
| 27.0         3367     0         143.3              |
+ -------------------------------------------------- +

MongoDB Module

Let's start by connecting to a MongoDB instance:

mongo:mongodb$> mconnect dev601

Next, let's choose a database to work in:

mongo:mongodb$> use shocktrade

Finally, let's retrieve a document. In this example, we'll retrieve a stock quote for Apple Inc. (ticker: AAPL)

mongo:mongodb$> mget Stocks { "symbol" : "AAPL" }
{
  "_id":{
    "$oid":"51002b2d84aebf0342cfb659"
  },
  "EBITDA":5.913E10,
  "active":true,
  "ask":98.77,
  "askSize":null,
  "assetClass":"Equity",
  "assetType":"Common Stock",
  "avgVolume":null,
  "avgVolume10Day":59306900,
  "avgVolume3Month":54995300,
  "baseSymbol":null,
  "beta":1.03,
  "bid":98.76,
  "bidSize":null,
  "bookValuePerShare":20.19,
  "businessSummary":"\n    Apple Inc. designs, manufactures, and markets personal computers and related personal computing and mobile communication devices along with a variety of related software, services, peripherals, and networking solutions. The Company sells its products worldwide through its online stores, its retail stores, its direct sales force, third-party wholesalers, and resellers.\n  ",
  "change":-0.42,
  "change52Week":44.37,
  "change52WeekHigh":null,
  "change52WeekLow":null,
  "change52WeekSNP500":16.41,
  "changePct":-0.42,
  "cikNumber":320193,
  "close":98.76
}

To view all of the MongoDB commands, use the

-m
switch and the module name (
mongodb
in this case):
mongo:mongodb$> ? -m mongodb
+ ------------------------------------------------------------------ +
| command   module   description                                     |
+ ------------------------------------------------------------------ +
| mconnect  mongodb  Establishes a connection to a MongoDB cluster   |
| mfindone  mongodb  Retrieves a document from MongoDB               |
| mget      mongodb  Retrieves a document from MongoDB               |
| mput      mongodb  Inserts a document into MongoDB                 |
| use       mongodb  Sets the current MongoDB database               |
+ ------------------------------------------------------------------ +            

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