jKool provides an application analytics SaaS for DevOps. These slides illustrate some of the choices we had to make and the architectural decisions to build a system for both real-time and historical application analytics.

1. How jKool Analyzes Streaming Data in Real
Time with DataStax
Charles Rich
VP of Product Management
jKool – jKoolcloud.com
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Webinar Housekeeping

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Agenda
• jKool Overview
• jKool Technology
• Challenges
• Why We Selected Cassandra and DataStax
• Demo

4. jKool Overview
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• jKool
– Founded 2014 as an spin-off from Nastel Technologies
– Expertize in building scalable real-time analytics
• Initial Vision
– Address the big data problems we saw at customers
• Inability to analyze data fast enough to take action and address problems
• Too much data – Too little time
– Provide real-time, in-memory analytics (our heritage)
– Leverage open-source
– SaaS (or on-premises)
– Simplicity

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What is jKool?
A solution to Find and Fix Problems Faster (operational intelligence)
DevOps can use jKool to get real-time diagnostics for entire
applications: logs, metrics and transactions.
– Detect anomalies, 2-clicks to root-cause
– Discover log, transaction topologies
– Analyze app behavior
– Diagnose and determine causality
• An alternative to Splunk or Elasticsearch
– Fraction of the cost of Splunk
– Much easier to use than Elasticsearch

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Business Value: Instant Insight
Provide high quality app experiences for customers -
Improve customer satisfaction
Enable DevOps to:
– Fix problems faster
• Faster problem resolution, eliminate false alarms
– Deliver releases sooner
• Less time patching and more time innovating
– Be proactive
• Spot trends and prevent problems

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Features
• Web-based, mobile-friendly dashboard
– Designed for simplicity and power
• Real-time & historical visualization
– Flexible, user configurable
• Analytics immediately detect outliers
– Aggregation, summarization, comparison, including: count, min,
max, avg., bucketing, filtering and Bollinger
• Ease of use
– Talk to your data using English-like query language
• Scale to handle the largest volumes of data
– NoSQL architecture provides elastic scalability

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jKool Does Machine Data
• Sequence, Order, Group, Store
• Relationships
• Compute Timing
• Summarization, comparisons
• Triggers based on continuous queries (CEP)
– Subscribe to events min elapsedtime, avg elapsedtime, max
elapsedtime where eventname="Buy" show as linechart

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Real-time, In-Memory
Analytics
jKool Analyzes
Time-Series Data

10. Technology
• Elastic Architecture
– Linear scalability – Highly
extensible
– Fast, in-memory analysis
• Open Source
– NoSQL DB, tools and
instrumentation
– No schema to maintain
• FatPipes
– Micro-services for ultimate
flexibility, change and configuration
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RESTful

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Key to Real-time Analytics
• Process streams as they come while at the same time
avoiding IO
– Streams are split into real-time queue and persistence queue
with eventual consistency
• Both have to be processed in parallel
– Writing to persistence layer and then analyzing will not achieve
near real-time processing

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Why clustered computing platforms?
• STORM paired with Kafka/JMS and CEP
– Clustered way to process incoming real-time streams
• STORM handles clustering/distribution
• Kafka/JMS for a messaging between grids
– Split streaming workload across the cluster
– Achieve linear scalability for incoming real-time streams
• Apache Spark (alternative to MapReduce)
– For distributing queries and trend analysis
– Micro batching for historical analytics
– Loading large dataset into memory (across different nodes)
– Running queries against large data-sets

13. Web Interface: DevOps Application Owner
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Challenges: Meeting our Objectives
• Store everything, analyze everything…
• Combined real-time & historical analytics
• Fast response, flexible query capabilities
– Target – for business user
– Insulate us from underlying software
– Hide complexity
• Scale for ingesting data-in-motion
• Scale for storing data-at-rest
• Elasticity & Operational efficiency
• Ease of monitoring & management

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Challenges: What we experienced
• So many technology options (…so little time…)
– Deciding on the right combination is key early on
• Cassandra/Solr deployment — (it was a learning experience for us)
– Lots of configuration, memory management, replication options
• Monitoring, managing clusters
– Cassandra/Solr, STORM, Zookeeper, Messaging
– +Leverage parent company’s AutoPilot Technology
• Achieving near real-time analytics proved
extremely challenging – but we did it!
– Keeping track of latencies across cluster
– Estimating computational capacity required to crunch incoming
streams

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Challenges: DB was the bottleneck
• Needed high performance DB platform
• SQL (Oracle, MySQL, etc.)
– No scale. We have had a lot of experience our customer’s issues with
this at our parent company Nastel…
– RAM was “the” bottleneck. Commits take too long and while that is
happening everything else stops
• NoSQL
– Cassandra/Solr (DSE)
– Hadoop/MapReduce
– MongoDB
• Clustered Computing Platforms
– STORM
– MapReduce
– Spark (we learned about this while building jKool)

17. Why we chose Cassandra/Solr?
• Pros:
– Simple to setup & scale for clustered deployments
– Scalable, resilient, fault-tolerant (easy replication)
– Ability to have data automatically expire (TTL – necessary for our pricing model)
– Configurable replication strategy
– Great for heavy write workloads
• Write performance was better than Hadoop.
• Insert rate was of paramount importance for us – get data in as fast as possible was our goal
• Java driver balances the load amongst the nodes in a cluster for us (master-slave would never have
worked for us)
– Solr provides a way to index all incoming data - essential
– DSE provides a nice integration between Cassandra and Solr
• Cons:
– Susceptible to GC pauses (memory management)
• The more memory the more GC pauses
• Less memory and more nodes seems a better approach than one big “honking” server (we see 6-8GB
optimal, so far)
– Data compaction tasks may hang
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Why not Hadoop MapReduce?
• MapReduce too slow for real-time workloads
– Ok for batch, not so great for real-time
– Need to be paired with other technologies for query (Hive/Pig)
– Complex to setup, run and operate
• Our goals were simplicity first…
• Opted for STORM/Spark wrapped with our own micro
services platform FatPipes instead of the Map Reduce
functionality

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Why we chose Cassandra/Solr vs. Mongo?
• Why not Mongo?
– Global write-lock performance concerns…
• Cassandra/Solr
– Java based (our project was in Java)
– Easy to scale, replicate data,
– Flexible write & write consistency levels (ALL, QUORUM, ANY, etc.)
– Did we say Java? Yes.(we like Java…)
• Flexible choice of platform coverage
– Great for time-series data streams (market focus for jKool)
• Inherent query limitations in Cassandra solved via Solr
integration (provided with DSE – as mentioned earlier)

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What we learned
• Consider your application
– Read heavy or write heavy? Both?
• Evaluate performance of course, but consider the user
– We needed simplicity: setup and scale (us and end user)
– We needed reliability – not planning on targeting data engineers
– We needed auto pruning (TTL)
– We needed easy search
• DSE had this…the others did not provide all of this
– We choose DSE.

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jKool in Real Time – A Live Demo

22. Thank you!
Input questions at any time
using the online interface
More information on jKool at: jKoolCloud.com