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Elasticsearch vs. InfluxDB For Time-Series Data

Time-series databases (TSDB) have grown in popularity, eclipsing even the use of Hadoop data stores. In particular, TSDBs are used for DevOps software development, asset tracking, and business intelligence. With voluminous incoming data from internet of things (IoT) devices, it’s essential that databases can handle large time-series records. Below, we explore InfluxDB and Elasticsearch, two data storage solutions that developers are increasingly adopting.

Elasticsearch Features and Use

Elasticsearch, based on the Apache Lucene library, premiered to great fanfare in 2010. Today, we commonly refer to it as ELK Stack since it includes the additional core products of Logstash and Kibana.

In particular, Elasticsearch is a free open search and analytics engine. It supports many types of data such as textual, numerical, geospatial, structured, and unstructured. Elasticsearch also supports many programming languages, including Java, JavaScript Go, .Net, PHP, Perl, Python, and Ruby.

Its speed and scalability promote efficient indexing of related content types. In fact, developers made it possible for the Elasticsearch index to be split into shards that replicate automatically, allowing greater horizontal scaling and data parallelization. Each shard comprises an Apache Lucene index. As a result of these versatile abilities, Elasticsearch is widely used for the following applications:

  • Application, website, and enterprise searches
  • Log analytics
  • Business and security analytics
  • Application performance and infrastructure metrics and container monitoring

Elasticsearch uses an inverted index data structure to enable rapid full-text searches. The inverted index contains a list of all unique words and the location of those words in any document.

How Elasticsearch Works

Within Elasticsearch, we store documents in indices. Each index is schema-free, allowing documents with varying structures to be used. When you insert documents, Elasticsearch splits the values of the document fields into tokens and adds these tokens into the inverted index. When someone searches a phrase, Elasticsearch splits the phrase into tokens and matches the tokens to the inverted index.

There are several benefits to implementing the Elasticsearch engine:

  • As a near real-time search platform, it’s well-suited for time-sensitive use cases such as security analysis and infrastructure monitoring.
  • The distributed nature of Elasticsearch allows data redundancy in cases of hardware failures.
  • Elasticsearch’s numerous features enable efficient storage and searching.
  • Data ingest, visualization, and reporting are simplified.

While Elasticsearch brings various features and benefits, it does present some restrictions.

  • Elasticsearch can’t effectively store large portions of binary data.
  • Elasticsearch V5.0 and later don’t contain site plugins such as Paramedic and Kopf and don’t provide support for them.
  • The transport client remains incompatible with cloud-hosted Elasticsearch clusters at V7.6 and later.

 InfluxDB For Time-Sensitive Data

InfluxData developed InfluxDB as a comprehensive platform to collect, store, analyze and visualize time-series data. Also known as the TICK stack, this time-series database technology is accurate to the nanosecond. This makes InfluxDB an ideal engine for the storage and analysis of sensor data with time stamps such as those used in IoT devices or scientific measuring instruments.

The four components of the TICK stack facilitate the collecting, storing, visualizing, and alerting of time-series data. They work seamlessly together. TICK comprises the following elements and functions.

  • Telegraf
    Collects time-series data from a variety of sources
  • InfluxDB
  • Chronograf
    Visualizes and graphs
  • Kapacitor
    Alerting, ETL, and detection of anomalies in time-series data

In particular, InfluxDB uses a built-in time service to ensure system synchronization.

InfluxDB’s retention policy is the engine’s most essential feature since it explicitly defines how long data will be kept. Take, for example, a typical application: real-time monitoring of server infrastructures for failure detection purposes. In this case, retaining the data for months proves unfeasible, so a smaller retention policy is defined. However, with IoT devices, data retention would be defined for a longer period.

Typical InfluxDB applications include the following:

  • DevOps monitoring of services and server clusters
  • Monitor IoT devices and sensors to ensure accurate and instant metrics
  • Time-series monitoring for industrial plants to monitor components in the production chain

Should You Choose Elasticsearch or InfluxDB for Time-Series Data Storage?

While both Elasticsearch and InfluxDB are robust time-series database engines, the decision to use one over the other comes down to application time dependency and data types. Each has its own set of benefits and restrictions. However, you can leverage both solutions in a single project.

InfluxDB is best for time-critical applications that need real-time querying. It can handle a higher number of writes than Elasticsearch. However, Elasticsearch is more suitable for textual data such as log messages, requests, and responses. Because it promotes textual data searching, Elasticsearch remains a superior option for querying by content.

Retaining data in InfluxDB while using Elasticsearch for metadata can be an effective solution for storing impeccable time-series records. Elasticsearch can quickly locate text-based events with timestamps of events and InfluxDB can then run calculations as the data comes in.

At Entrance Consulting, we assist companies in the purchase, building, and integration of software apps for efficient business operations. Contact us for more information on our complete line of custom software and app development solutions today.

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