Splunk Conf 2014 - Getting the message
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This document contains an agenda and summaries of sections from a presentation on messaging architectures and using messaging data with Splunk. The agenda includes an overview of common messaging systems like JMS, AMQP, and Kafka. It also covers customizing message handling and scaling modular inputs. Additionally, it discusses using ZeroMQ to access messages and using underutilized computers with JMS tasks.
![Java Example using SDK to load ZeroMQ
String query=search;
Job job = service.getJobs().create(query, queryArgs);
while (!job.isDone()) {
32
Thread.sleep(100);
job.refresh();
}
// Get Query Results and store in String str⊠(Code Omitted)
// Assuming single line events
StringTokenizer st = new StringTokenizer(str, "n");
while(st.hasMoreTokens()) {
String temp= st.nextToken();
sock.send(temp.getBytes(), 0);
byte response[] = sock.recv(0);
}](https://image.slidesharecdn.com/gettingthemessage-141013104536-conversion-gate01/85/Splunk-Conf-2014-Getting-the-message-32-320.jpg)
Splunk Conf 2014 - Getting the message
- 1. Copyright © 2014 Splunk Inc. Getting the Message Damien Dallimore Dev Evangelist , CSO Office @ Splunk Nimish Doshi Principal Systems Engineer @ Splunk
- 2. Disclaimer During the course of this presentation, we may make forward looking statements regarding future events or the expected performance of the company. We caution you that such statements reflect our current expectations and estimates based on factors currently known to us and that actual events or results could differ materially. For important factors that may cause actual results to differ from those contained in our forward-looking statements, please review our filings with the SEC. The forward-looking statements made in the this presentation are being made as of the time and date of its live presentation. If reviewed after its live presentation, this presentation may not contain current or accurate information. We do not assume any obligation to update any forward looking statements we may make. In addition, any information about our roadmap outlines our general product direction and is subject to change at any time without notice. It is for informational purposes only and shall not, be incorporated into any contract or other commitment. Splunk undertakes no obligation either to develop the features or functionality described or to include any such feature or functionality in a future release. 2
- 3. Agenda 3 Damienâs Section What is messaging JMS + Demo AMQP + Demo Kafka + Demo Custom message handling Architecting for scale Nimishâs Section Using ZeroMQ Using JMS for underutilized computers Question time
- 5. 5 From Middle Earth Make Splunk Apps & Add-ons Messaging background
- 6. 6
- 8. What is messaging ? Messaging infrastructures facilitate the sending/receiving of messages between distributed systems Message can be encoded in one of many available protocols A common paradigm involves producers and consumers exchanging via topics or queues 8 Topics (publish subscribe) Queues (point to point) TOPIC QUEUE
- 9. Why are messaging architectures used ? Integrating Legacy Systems Integrating Heterogeneous Systems Distributed Applications Cluster Communication High Performance Streaming 9
- 10. Thereâs a lot of information in the pipes 10
- 11. The data opportunity Easily tap into a massive source of valuable inflight data flowing around the veins Donât need to access the application directly ,pull data off the messaging bus I can not think of a single industry vertical that does not use messaging 11
- 12. Getting this data into Splunk Many different messaging platforms and protocols JMS (Java Message Service) AMQP (Advanced Message Queueing Protocol) Kafka Nimish will cover some more uses cases also 12
- 13. JMS Not a messaging protocol , but a programming interface to many different underlying message providers WebsphereMQ , Tibco EMS , ActiveMQ , HornetQ , SonicMQ etc⊠Very prevalent in the enterprise software landscape DEMO 13
- 14. AMQP RabbitMQ Supports AMQP 0.9.1, 0.9, 0.8 Common in financial services and environments that need high performance and low latency DEMO 14
- 15. Kafka Cluster centric design = strong durability and fault tolerance Scales elastically Producers and Consumers communicate via topics in a Kafka node cluster Very popular with open source big data / streaming analytics solutions DEMO 15
- 16. Custom message handling These Modular Inputs can be used in a multitude of scenarios Message bodies can be anything : JSON, XML, CSV, Unstructured text, Binary Need to give the end user the ability to customize message processing So you can plugin your own custom handlers Need to write code , but it is really easy , and there are examples on GitHub Iâm a big data pre processing fan 16
- 17. Cut the code 17
- 18. Compile, bundle into jar file, copy to Splunk 18
- 19. Declaratively apply it Letâs see if it works 19
- 20. Achieving desired scale AMQP Mod Input AMQP Queue 20 Single Splunk Instance With 1 Modular Input instance , only so much performance / throughput can be achieved Youâll hit limits with JVM heap , CPU , OS STDIN/STDOUT Buffer , Splunk indexing pipeline
- 21. So go Horizontal AMQP Queue 21 Splunk Indexer Cluster Universal Forwarders AMQP Broker AMQP Mod Input AMQP Mod Input
- 23. About Me âą Principal Systems Engineer at Splunk in the NorthEast âą Session Speaker at all past Splunk .conf user conferences âą Catch me on the Splunk Blogs 23
- 24. Problem with Getting Business Data from JMS The goal is to index the business message contents into Splunk Message Uncertainty Principal: If you de-queue the message to look at it, you have affected the TXN If you use various browse APIs for content, you may miss it â Message may have already been consumed by TXN Suggestion: Use a parallel queue to log the message â Suggestion: Try ZeroMQ 24
- 25. Why use ZeroMQ Light Weight Multiple Client language support (Python, C++, Java, etc) Multiple design patterns (Pub/Sub, Pipeline, Request/Reply, etc) Open Source with community support 25
- 26. Application Queue and ZeroMQ Example 26 Auto Load Balance 1 2
- 27. Example Python Sender context = zmq.Context() socket = context.socket(zmq.PUSH) socket.connect('tcp://127.0.0.1:5000') sleeptime=0.5 27 while True: num=random.randint(50,100) now = str(datetime.datetime.now()) sleep(sleeptime) payload = now + " Temperature=" + str(num) socket.send(payload)
- 28. Python Receiver (Scripted Input) context = zmq.Context() socket = context.socket(zmq.PULL) # Change address and port to match your environment socket.bind("tcp://127.0.0.1:5000") 28 while True: msg = socket.recv() print "%s" % msg except: print "exception"
- 29. Python Subscriber (Scripted Input) context = zmq.Context() socket = context.socket(zmq.SUB) socket.connect ("tcp://localhost:5556") # Subscribe to direction filter = "east" socket.setsockopt(zmq.SUBSCRIBE, filter) 29 while True: string = socket.recv() print string
- 30. Parallel Pipeline Example 30
- 31. Getting Events out of Splunk 31 Splunk SDK Use Cases: â In Depth processing of Splunk events in a queued manner â Use as pivot point to drop off events into a Complex Event Processor â Batch Processing of Splunk events outside of Splunk ïȘ Divide and Conquer Approach as seen in last slide
- 32. Java Example using SDK to load ZeroMQ String query=search; Job job = service.getJobs().create(query, queryArgs); while (!job.isDone()) { 32 Thread.sleep(100); job.refresh(); } // Get Query Results and store in String str⊠(Code Omitted) // Assuming single line events StringTokenizer st = new StringTokenizer(str, "n"); while(st.hasMoreTokens()) { String temp= st.nextToken(); sock.send(temp.getBytes(), 0); byte response[] = sock.recv(0); }
- 33. Idle Computers at a Corporation 33 âŠ
- 34. Idea: Use Ideas from SETI @ Home 34
- 35. Idle Computers Put to Work Using JMS 35 âŠ
- 36. Applications for Distributing Work Application Server would free up computing resources Work could be pushed to underutilized computers Examples: â Massive Mortgage Calculation Scenarios â Linear Optimization Problems â Matrix Multiplication â Compute all possible paths for combinatorics 36
- 38. Algorithm Application servers push requests to queues, which may include data in the request object called a Unit of Work JMS client implements doWork() interface to work with data Message Driven Bean receives finished work and implements doStore() interface What does this have to do with Splunk? â Time Series results can be stored in Splunk for further or historical analytics 38
- 39. Matrix Example High Level Architecture 39
- 40. Search Language Against Matrix Result List Column Values of Each Stored Multiplied Matrix using Multikv 40 Screenshot here
- 41. Search Language Against Matrix Result Visualize the Average for Columns 2 to 5 41 Screenshot here
- 42. Search Language Against Matrix Result Perform arbitrary math on aggregate columns 42 Screenshot here
- 43. Reference ZeroMQ â http://apps.splunk.com/app/1000/ â Blog: http://blogs.splunk.com/2012/06/08/zeromq-as-a-splunk-input/ Using JMS for Underutilized Computers â Github Reference: https://github.com/nimishdoshi/JMSClientApp/ â Blog: http://blogs.splunk.com/2014/04/11/splunk-as-a-recipient-on-the-jms-grid/ â Article:http://www.oracle.com/technetwork/articles/entarch/jms-distributed-work- 082249.html 43
- 44. Questions ?
- 45. THANK YOU ddallimore@splunk.com ndoshi@splunk.com
Editor's Notes
- #6: From Auckland Dev evang , ex customer 5th Conf Make Apps , Cut code Through messaging background , a lot of integration work in many different industrys , particularly in the enterprise Java space.
- #7: Everything 100% open source use , reuse , whatever. Collaborate Community answers.splunk.com for support is best
- #10: Enterprise Service Buses Multi tier apps ,asynch processing Apache Storm That pretty broadly covers most enterprise software scenarios.
- #14: Interoperablity not guaranteed message producers and consumers may be implemented differently You âpluginâ the underlying message provider implemention
- #15: Wire level protocol, hence better interoperabilty than JMS and better performance Usual messaging features such as , Flow control , guaranteed delivery, quality of service etc⊠JP Morgan chase 1.0 is an entirely different protocol , any demand for this ?? Swiftmq Apache apollo Apache qpid
- #16: Manage access to the cluster with Apache Zookeeper Data streams can be partitioned over multiple machines in the cluster Apache storm spout
- #17: If you have to opportunity to get the data into an optimal format for Splunk , do it. Handle custom payloads , even binary Efficient use of license Pre compute some values that might not be best suited to the Splunk search language
- #18: Inputting the setting into stanza Send message Show reversed output
- #20: com.splunk.modinput.jms.customhandler.MessageTextReverser
- #21: Same pattern applys to JMS and Kafka
- #22: Your only limits are going to be your ability to provision Splunk nodes. Same pattern applys to other Mod Inputs Works with queues , not pub sub topics (youâll get duplicates)