Much has been said about the importance of big data analysis and the numerous benefits enterprises gain from machine data – enhanced security, smart resource allocation, improved customer experience, and facilitated “Internet of Things” interactions being just some of them. Yet, to gain insights from big data, organizations have to run applications that conduct machine data analysis (such as Splunk or Hadoop). To ensure seamless operations of such applications, it is important to understand their architectural constraints.
Splunk Enterpise, is quickly becoming the standard for machine data analysis, provides real-time security threat detection, manages resource utilization and consumption, and promotes machine-to-machine and machine-to-human interactions – the so-called operational intelligence.
Splunk software is a search engine for all types of machine generated data. But before being presented to a user in a readable form, big data needs to be indexed, processed and analyzed. Capturing, analyzing and categorizing terabytes and petabytes of data before turning into valuable information requires a specific architecture. Splunk uses the Map Reduce framework for these purposes – a programming model that has originally emerged as a proprietary Google technology. Essentially, Splunk architecture is based on three main components: forwarders, indexers and search heads.
Forwarders are agents that perform data collection, data load balancing and forward data to indexers. Indexers then write data to storage and performs much of the work related to running searches. Search heads send search requests to the indexers to then send the results back to the users.
5 Splunk Architecture Considerations
In order for Splunk to satisfy user demands with acceptable performance, there are considerations that must be addressed before implementing the proper infrastructure:
1. The amount of incoming data
Incoming data amounts are not created equal, and more data requires more time to process it into searchable info.
2. The amount of indexed data
Increasing amounts of indexed data require increased I/O bandwidth for data storage and providing search results.
3. Number of concurrent users
If an instance of Splunk is used by more than one user simultaneously, this instance will require more resources to perform searches and generate alerts, reports and dashboards.
4. Number of saved searches
High number of searches performed over a certain period of time requires more capacities and resources.
5. Types of searches
Another deciding factor influencing the speed of Splunk performance is the type of conducted searches. As mentioned above, some searches require more I/O bandwidth, some – more CPU resources.
With these specific considerations, comes multi-dimensional planning that is required not only for the Splunk architecture, but for the underlying infrastructure.
Whether you are deploying or scaling Splunk you will run into these commonly unforeseen challenges:
- Timely and costly training of staff to properly provision the environment
- Ingest constraints because of low I/O bandwidth and latency
- Inadequate performance because of storage IO limitations
- Inadequate system resources including CPU and RAM to support indexers and search heads
- Not enough capacity to support the growing environment
So what if there was a Splunk-ready platform that could eliminate these challenges?
Now, there is. Cloudistics offers a complete plug-and-play platform that delivers Splunk out of the box. With tightly integrated server, storage, networking, virtualization and management, the Cloudistics platform delivers a hyperscale platform pre-built to perform for Splunk. De-signed with intelligent software and enterprise-grade hardware, Cloudistics delivers the all-flash speeds and high bandwidth needed to scale from GBs of ingest to TBs a day.
Cloudistics eliminates these challenges by:
- Due to Cloudistics pre-templated Splunk search heads and indexers, along with it’s easy-to-use management portal, IT staff training is minimal, saving you time and money.
- Cloudistics network virtualization technology delivers high throughput enabling you to ingest 1.4TB-15TB of data a day. (most vendors offer up to 1TB/day).
- With all-flash storage built-in, Cloudistics delivers microsecond latency resulting in op-timal indexing and search operations.
- You’ll never have to worry about not having enough CPU and RAM, Cloudistics’ built-in hypervisor delivers low overhead and maximum resource utilization.
- To tie this all together, Cloudistics was built for scale. Start small and simply scale up individual storage, networking and compute resources at a time without downtime or performance degradation.
Now that you’ve learned the challenges of Splunk and the infrastructure implications, tune in next week to learn more about how the Cloudistics platform truly is a ‘Slam Dunk for Splunk.’