Elasticsearch vs Loki

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Elasticsearch

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Loki

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Elasticsearch vs Loki: What are the differences?

Introduction

Elasticsearch and Loki are both open-source log aggregation systems, but they have some key differences in terms of their architecture, query language, and scalability.

  1. Query Language: Elasticsearch uses a JSON-based query language called Query DSL, which provides a flexible and powerful way to search and filter data. On the other hand, Loki uses a label-based query language called LogQL, which is designed specifically for log data and allows users to filter logs based on their metadata labels.

  2. Data Storage: Elasticsearch stores data in an inverted index, which allows for efficient full-text search and aggregations. It also supports sharding and replication, enabling horizontal scalability. In contrast, Loki stores logs as chunks of binary data in an object storage system like Amazon S3 or Google Cloud Storage. This approach optimizes for efficient storage and retrieval of log data.

  3. Scalability: Elasticsearch is horizontally scalable and can handle large volumes of data by distributing it across multiple nodes in a cluster. It also supports data replication for high availability. Loki, on the other hand, is designed to be lightweight and horizontally scalable by leveraging object storage. It can handle high load but may require additional components like Grafana Tempo for distributed tracing.

  4. Log Storage Lifetime: Elasticsearch is typically used for long-term log storage, allowing users to retain logs for extended periods of time. This makes it suitable for compliance or auditing purposes. Meanwhile, Loki is more focused on providing real-time log aggregation and analysis, with a shorter log storage lifetime. It prioritizes recent log data to provide real-time insights.

  5. Log Structure: Elasticsearch is schemaless and can handle logs with varying structures. It automatically indexes log fields for efficient searching and filtering. On the other hand, Loki assumes that logs have a consistent structure and does not automatically index log fields. It relies on labels for querying and filtering log data.

  6. Integration with Ecosystem: Elasticsearch is part of the Elastic Stack, which includes tools like Logstash and Kibana. Logstash provides log ingestion and transformation capabilities, while Kibana offers a visual interface for log exploration and analysis. Loki is part of the Grafana ecosystem, integrating seamlessly with Grafana for log visualization and analysis.

In summary, Elasticsearch and Loki differ in their query languages, data storage mechanisms, scalability approaches, log storage lifetimes, log structure assumptions, and integration with their respective ecosystems.

Advice on Elasticsearch and Loki
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 365.9K views
Needs advice
on
AlgoliaAlgoliaElasticsearchElasticsearch
and
FirebaseFirebase

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 8 upvotes · 271.2K views
Recommends
on
AlgoliaAlgolia

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

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Mike Endale
Recommends
on
Cloud FirestoreCloud Firestore

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

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Pros of Elasticsearch
Pros of Loki
  • 326
    Powerful api
  • 315
    Great search engine
  • 230
    Open source
  • 214
    Restful
  • 199
    Near real-time search
  • 97
    Free
  • 84
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 3
    Highly Available
  • 3
    Awesome, great tool
  • 3
    Great docs
  • 3
    Easy to scale
  • 2
    Fast
  • 2
    Easy setup
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Great piece of software
  • 2
    Reliable
  • 2
    Potato
  • 2
    Nosql DB
  • 2
    Document Store
  • 1
    Not stable
  • 1
    Scalability
  • 1
    Open
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Easy to get hot data
  • 0
    Community
  • 5
    Opensource
  • 3
    Very fast ingestion
  • 3
    Near real-time search
  • 2
    Low resource footprint
  • 2
    REST Api
  • 1
    Smart way of tagging
  • 1
    Perfect fit for k8s

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Cons of Elasticsearch
Cons of Loki
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
    Be the first to leave a con

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    - No public GitHub repository available -

    What is Elasticsearch?

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

    What is Loki?

    Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus. It is designed to be very cost effective and easy to operate, as it does not index the contents of the logs, but rather a set of labels for each log stream.

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    What tools integrate with Elasticsearch?
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    Blog Posts

    May 21 2019 at 12:20AM

    Elastic

    ElasticsearchKibanaLogstash+4
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    GitHubPythonReact+42
    49
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    GitHubPythonNode.js+47
    54
    72280
    What are some alternatives to Elasticsearch and Loki?
    Datadog
    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
    Solr
    Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
    Lucene
    Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Algolia
    Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.
    See all alternatives