Developing software is an art in itself. From building to shipping, developers have to keep iterating the process to make improvements to the existing ones. Developers around the world spend hours on building quality logging into their applications. But this logging is only efficient when we have one-page or two-page applications where debugging through logs is relatively easy.
But while building large-size applications, quality logging requires a search where developers don’t have to comb through all the logs one by one. Simply a search will do the work. In this article, we will be discussing two of the most popular software built for log management - Elasticsearch and Splunk. Before comparing the two, let us first take an overview of both tools.
The ELK stack stands for Elasticsearch, Logstash, and Kibana, and now also includes Beats. These tools together create a powerful log analytic tool known as ELK stack and are maintained by Elastic (the company behind ELK stack). Let’s have a brief overview of each of the components.
Elasticsearch does more than log analysis. It is a powerful search engine that makes the search easier anywhere. Elasticsearch is a NoSQL database built on the Lucene search engine.
Logstash is used to aggregate and process data and send it to Elasticsearch. It is an open-source, data processing pipeline that ingests data from several sources simultaneously, transforms it, and then sends it to get collected.
Kibana provides a user interface that works on top of Elasticsearch and lets users analyze data using visualizations and dashboards. It provides real-time histograms, line graphs, pie charts, and maps.
The ELK stack allows users to ingest data from any source (integration of cloud environments is supported too) in any form and lets a user search, analyze, and visualize log data in real-time.
Splunk is not open-source like the ELK stack. It is built and managed by a company with the same name which is totally focused on log analysis and observability. It is very easy to plug into a client’s product but comes with a hefty price structure.
Three key components in Splunk are its forwarder, indexer, and search head. The forwarder pushes data to a remote indexer. The indexer manages all the indexing and search queries. The search head is the front-end web interface where these 3 components can be combined.
Now that we have an overview of both tools let’s discuss the key differences between them.
Since Splunk is proprietary software, it is easier to configure and set up than the ELK Stack. Both ELK and Splunk support on-premise and SaaS deployment, which means that the software can sit on the user’s physical data center and the user can also deploy these both to the cloud.
In Splunk, data is stored in indexes made up of file buckets. These buckets contain data structures that enable Splunk to determine if the data includes terms or words. Buckets also contain compressed, raw data. This data is usually reduced to 15% of its original size, once compressed, to help Splunk store data efficiently.
Elasticsearch stores data as unstructured JSON documents. Each document correlates a set of keys (names of fields or properties) with their corresponding values (strings, numbers, Booleans, dates, arrays of values, geolocations, or other types of data). It indexes the full contents of stored documents. It makes documents fully searchable while requiring more storage space
The query syntax on Kibana is based on the Lucene Query syntax while Splunk uses its own Search Processing Language (SPL). Lucene query syntax is pretty much similar to scripting languages hence easy to learn for anyone who has worked with scripting languages. SPL on the other side is a proprietary language that supports the search pipeline.
The key difference between SPL syntax and Lucene queries is that SPL supports the search pipeline where consecutive commands are chained together using a pipe character that allows the output of one command to be used as the input of the next one. Lucene query syntax is more straightforward and can directly generate output from the query.
Indexing in Elasticsearch is a collection of correlated documents. It uses a data structure known as an inverted index, which is designed to provide full-text searches. The data structure lists every unique word that appears in any document and identifies all of the documents each word occurs in. This indexing is done using the Index API, where a user can add or update a JSON document in a specific index.
Splunk uses an indexer to index data coming from the Splunk forwarder. The indexer breaks logs data into lines and identifies timestamps to create individual events. It then annotates them with metadata. Data is parsed only if it is from a universal forwarder else it directly indexes the data. It then transforms event data using transformation rules defined by an operator. Finally, Splunk writes the parsed events to disk, pointing to them from an index file which enables fast search across huge data volumes.
Another benefit of Splunk Indexer is data replication. Splunk keeps multiple copies of indexed data hence no need to worry about data loss.
The main difference in the user interface is that Splunk web UI or the search head provides user management and controls whereas Kibana does not support this feature.
Kibana offers all the features to build dashboards quickly. But one thing that users have to make sure of is the data types. Data types have to be correct else the aggregator functions won’t work. Data filtering is much easier and more advanced in the ELK stack.
As per the official documents of Splunk, data collection depends on what type of data source a client is using. These are the methods that Splunk uses for data collection.
- Ingest service which collects JSON objects from /events and /metrics endpoint of the ingest REST API.
- Forwarder service which collects data from the Splunk forwarder.
- DSP HTTP event collector which collects data from HTTP clients and Syslog data sources.
- DSP Collect connectors that collect data from several types of data sources such as Amazon S3, Amazon CloudWatch, Azure, etc. These collectors collect data through jobs that run on a schedule.
- Steaming connectors collect data from several data sources such as Apache Kafka, Apache Pulsar, Google Cloud Pub/Sub, etc. These types of connectors receive data continuously emitted by the sources.
In Elasticsearch, REST calls are one of the ways to collect data and ingest it into Elasticsearch. Elastic Beats is another way for data collection. Beats would sit on the client’s device locally, collect all of their logs, and then send them into the aggregator (Logstash).
If you are using the free version of ELk stack, then you don't have to pay for any license. But Elasticsearch needs a lot of support and maintenance which you will have to account for. Splunk is a costly tool used by enterprises. You can find its pricing details here.
What to choose between Elasticsearch and Splunk? It really depends on how your organization is structured and how much time are you willing to invest in setting up log management. Splunk is easy to plug in but can be costly. ELK stack on the other hand is free and open source but requires more work and planning at the beginning for setup. It also needs resources for maintenance.
You can also try out SigNoz. SigNoz can be a good choice for users who don’t have the resources to support the Elasticsearch stack and don't need the many add-ons that Splunk offers.
SigNoz is a full-stack open-source APM that you can use as an alternative to Splunk and Elasticsearch.
SigNoz uses a columnar database ClickHouse to store logs, which is very efficient at ingesting and storing logs data. Columnar databases like ClickHouse are very effective in storing log data and making it available for analysis.
Big companies like Uber have shifted from the Elastic stack to ClickHouse for their log analytics platform. Cloudflare too was using Elasticsearch for many years but shifted to ClickHouse because of limitations in handling large log volumes with Elasticsearch.
The logs tab in SigNoz has advanced features like a log query builder, search across multiple fields, structured table view, JSON view, etc.
You can also view logs in real time with live tail logging.
With advanced Log Query Builder, you can filter out logs quickly with a mix and match of fields.
SigNoz can be installed on macOS or Linux computers in just three steps by using a simple install script.
The install script automatically installs Docker Engine on Linux. However, on macOS, you must manually install Docker Engine before running the install script.
git clone -b main https://github.com/SigNoz/signoz.git cd signoz/deploy/ ./install.sh
You can visit our documentation for instructions on how to install SigNoz using Docker Swarm and Helm Charts.
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