Stop Hoarding! Take Control Of Network Data with Elasticsearch ILM

February 29, 2024

Stop Hoarding! Take Control Of Network Data with Elasticsearch ILM

In today's evolving digital landscape, data is cemented as the new currency, fueling innovation and driving business decisions. However, as we bask in the wealth of information, we're also facing a formidable challenge: managing the deluge of enterprise data. This burgeoning data tide brings with it a complexity and cost that can’t be overlooked. Navigating this landscape, where data is both a treasure trove and a formidable wave to tame, has become the pivotal task for businesses striving to stay ahead in the age of big data.

To meet the demands of big data, from storage to real-time search, analysis, and visualization, Elasticsearch has emerged as a strong contender. In fact, organizations are turning to Elasticsearch to help them manage their vast amount of NetFlow data that they are collecting with ElastiFlow. This allows companies to gain valuable insights from their data quickly and easily. 

ElastiFlow has deep expertise in working with open data platforms and especially Elasticsearch. This post shares some best practices for implementing and automating Elasticsearch ILM (Index Lifecycle Management) so you can reduce the risk of drowning in a massive lake of data and/or exploding your storage budget. There are other techniques for reducing the amount of flow data collected, without resorting to sampling data, including our implementation of Elastic TSDS - this Elastiflow post explains

Understanding Data Management Challenges

Good data management is essential for businesses to stay efficient, secure their data, and stay privacy compliant. Legacy data management strategies simply can't keep up with rapid data growth, causing higher costs, degraded performance, and difficult data access. Also, treating all data the same regardless of its importance over time doesn't work well, showing the need for a flexible and scalable approach. A smart data management policy is key to effective data control. It needs to cover how data is collected, stored, kept, archived, and deleted, matching the unique needs and rules of the organization.

Solving Data Management Challenges with ILM in Elasticsearch

Index Lifecycle Management (ILM) in Elasticsearch provides a smart way to handle data within an Elasticsearch cluster. It automatically moves data through different stages - hot, warm, cold, and delete - according to set rules. This makes sure data is kept in the most cost-saving storage without losing easy access or speed. More detailed information on Elasticsearch ILM can be found on this Elastic docs page.

Hot Phase: Data that is frequently accessed and written is stored on high-performance storage. This phase is optimized for speed and immediate availability.

Warm Phase: As data ages and is accessed less frequently, it is moved to less expensive storage. It remains searchable but does not require the high performance of the hot phase.

Cold Phase: Older data that is rarely accessed is further moved to even cheaper storage options. It is still searchable but stored in a way that minimizes costs.

Delete Phase: Ultimately, data that is no longer needed or has surpassed its regulatory retention period is securely deleted.

Implementing ILM with Elasticsearch: A Basic Example

An Elasticsearch ILM policy might look something like this:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 { "policy": { "phases": { "hot": { "min_age": "0ms", "actions": { "rollover": { "max_age": "30d" }, "set_priority": { "priority": 100 }, "allocate": { "include": {}, "exclude": {}, "require": { "storage": "ssd" } } } }, "warm": { "min_age": "30d", "actions": { "set_priority": { "priority": 50 }, "allocate": { "include": {}, "exclude": {}, "require": { "storage": "hdd" } }, "shrink": { "number_of_shards": 1 }, "forcemerge": { "max_num_segments": 1 } } }, "cold": { "min_age": "60d", "actions": { "set_priority": { "priority": 20 }, "allocate": { "include": {}, "exclude": {}, "require": { "storage": "cold" } }, "freeze": {} } }, "delete": { "min_age": "90d", "actions": { "delete": { "delete_searchable_snapshot": true } } } } } }
  • Hot Phase: Store newly indexed data on fast SSDs for 30 days.

  • Warm Phase: After 30 days, move data to slower, less expensive HDDs, and reduce replicas to minimize storage costs.

  • Cold Phase: After 60 days, move data to cold storage, optimizing for cost over performance.

  • Delete Phase: Delete data that is over 90 days old or meets certain criteria, ensuring compliance with data retention policies.

This automated lifecycle management simplifies data handling, reduces manual intervention, and ensures that data is stored in the most cost-effective manner throughout its lifecycle.


Using an Elasticsearch ILM policy is a valuable tool to help you manage your data storage cost effectively. It cuts storage costs, speeds up data access, and meets data keeping rules by automating how data is handled. This improves customer experience with quicker, dependable information access, eases the technical team's work, and makes budgeting better for the finance department.

By the way, collecting network flow data (Netflow) with Elasticsearch can quickly create a data management nightmare. That’s why ElastiFlow makes Elasticsearch ILM even easier by automatically creating an ILM policy on installation.

Don't let problems with managing data slow down your business. Use Elasticsearch ILM to make your data work for you. ElastiFlow helps feed your network data into Elasticsearch, letting you manage and watch over your network better. Getting started with ElastiFlow takes minutes and is free for up to 4,000 flow records per second. We also offer a 30-day free trial. More detailed pricing and support platforms are available on our website Subscription page. Thanks for reading!

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