Elasticsearch vs Loggly

Need advice about which tool to choose?Ask the StackShare community!

Elasticsearch

34.1K
26.6K
+ 1
1.6K
Loggly

274
302
+ 1
168
Add tool

Elasticsearch vs Loggly: What are the differences?

  1. 1. Scalability and Performance: Elasticsearch is designed for horizontal scalability and can handle large amounts of data and traffic. It uses sharding and replication to distribute data across multiple nodes and ensure high availability. On the other hand, Loggly is a cloud-based log management service that does not offer the same scalability and performance as Elasticsearch. It relies on its own infrastructure to process and index logs, which may not be as efficient as Elasticsearch's distributed architecture.

  2. 2. Full-Text Search and Analytics: Elasticsearch is primarily built for full-text search and analytics. It uses inverted indices to provide fast search capabilities across large volumes of structured and unstructured data. Loggly, on the other hand, focuses specifically on log management and analytics. It provides pre-built dashboards and visualizations tailored for log analysis, making it easier for users to monitor and troubleshoot their systems.

  3. 3. Data Retention and Storage: Elasticsearch allows users to define data retention policies and manage the storage of their data through index lifecycle management (ILM). This gives users control over how long data is retained and how it is stored (e.g., in hot, warm, or cold storage). Loggly, on the other hand, provides a fixed retention period based on the user's subscription plan. Users have limited control over how long their logs are retained and cannot customize storage options.

  4. 4. Log Collection Methods: Elasticsearch supports various log collection methods, including agents, plugins, and integrations with external systems. It can ingest logs from different sources and protocols, making it a versatile solution for log management. Loggly, on the other hand, provides a centralized log collection system that relies on log shipping agents. While it supports popular log shipping methods like Syslog and Logstash, it does not offer the same level of flexibility as Elasticsearch.

  5. 5. Querying and Filtering: Elasticsearch provides a powerful query DSL (Domain-Specific Language) that allows users to perform complex searches and aggregations on their data. It supports filtering based on various criteria, such as time range, keyword matching, and numeric ranges. Loggly, on the other hand, offers a simplified query language that is focused on log analysis. It provides predefined search filters and allows users to perform basic search queries but lacks the advanced querying capabilities of Elasticsearch.

  6. 6. Security and Access Control: Elasticsearch offers robust security features, including authentication, authorization, and encryption. It supports role-based access control (RBAC) and integrates with external authentication providers like Active Directory and LDAP. Loggly, on the other hand, provides basic security features like HTTPS encryption and IP whitelisting but does not offer the same level of fine-grained access control as Elasticsearch. It does not support RBAC or integration with external authentication providers.

In Summary, Elasticsearch and Loggly differ in terms of scalability and performance, full-text search and analytics capabilities, data retention and storage options, log collection methods, querying and filtering capabilities, and security and access control features.

Advice on Elasticsearch and Loggly
André Ribeiro
at Federal University of Rio de Janeiro · | 4 upvotes · 49.1K views

Hi, community, I'm planning to build a web service that will perform a text search in a data set off less than 3k well-structured JSON objects containing config data. I'm expecting no more than 20 MB of data. The general traits I need for this search are: - Typo tolerant (fuzzy query), so it has to match the entries even though the query does not match 100% with a word on that JSON - Allow a strict match mode - Perform the search through all the JSON values (it can reach 6 nesting levels) - Ignore all Keys of the JSON; I'm interested only in the values.

The only thing I'm researching at the moment is Elasticsearch, and since the rest of the stack is on AWS the Amazon ElasticSearch is my favorite candidate so far. Although, the only knowledge I have on it was fetched from some articles and Q&A that I read here and there. Is ElasticSearch a good path for this project? I'm also considering Amazon DynamoDB (which I also don't know of), but it does not look to cover the requirements of fuzzy-search and ignore the JSON properties. Thank you in advance for your precious advice!

See more
Replies (3)
Roel van den Brand
Lead Developer at Di-Vision Consultion · | 3 upvotes · 38.9K views
Recommends
on
Amazon AthenaAmazon Athena

Maybe you can do it with storing on S3, and query via Amazon Athena en AWS Glue. Don't know about the performance though. Fuzzy search could otherwise be done with storing a soundex value of the fields you want to search on in a MongoDB. In DynamoDB you would need indexes on every searchable field if you want it to be efficient.

See more
Ted Elliott

I think elasticsearch should be a great fit for that use case. Using the AWS version will make your life easier. With such a small dataset you may also be able to use an in process library for searching and possibly remove the overhead of using a database. I don’t if it fits the bill, but you may also want to look into lucene.

I can tell you that Dynamo DB is definitely not a good fit for your use case. There is no fuzzy matching feature and you would need to have an index for each field you want to search or convert your data into a more searchable format for storing in Dynamo, which is something a full text search tool like elasticsearch is going to do for you.

See more
Julien DeFrance
Principal Software Engineer at Tophatter · | 3 upvotes · 37.6K views

The Amazon Elastic Search service will certainly help you do most of the heavy lifting and you won't have to maintain any of the underlying infrastructure. However, elastic search isn't trivial in nature. Typically, this will mean several days worth of work.

Over time and projects, I've over the years leveraged another solution called Algolia Search. Algolia is a fully managed, search as a service solution, which also has SDKs available for most common languages, will answer your fuzzy search requirements, and also cut down implementation and maintenance costs significantly. You should be able to get a solution up and running within a couple of minutes to an hour.

See more
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 6 upvotes · 375.7K 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!

See more
Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 8 upvotes · 279.9K 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.

See more
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.

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Elasticsearch
Pros of Loggly
  • 328
    Powerful api
  • 315
    Great search engine
  • 231
    Open source
  • 214
    Restful
  • 200
    Near real-time search
  • 98
    Free
  • 85
    Search everything
  • 54
    Easy to get started
  • 45
    Analytics
  • 26
    Distributed
  • 6
    Fast search
  • 5
    More than a search engine
  • 4
    Great docs
  • 4
    Awesome, great tool
  • 3
    Highly Available
  • 3
    Easy to scale
  • 2
    Potato
  • 2
    Document Store
  • 2
    Great customer support
  • 2
    Intuitive API
  • 2
    Nosql DB
  • 2
    Great piece of software
  • 2
    Reliable
  • 2
    Fast
  • 2
    Easy setup
  • 1
    Open
  • 1
    Easy to get hot data
  • 1
    Github
  • 1
    Elaticsearch
  • 1
    Actively developing
  • 1
    Responsive maintainers on GitHub
  • 1
    Ecosystem
  • 1
    Not stable
  • 1
    Scalability
  • 0
    Community
  • 37
    Centralized log management
  • 25
    Easy to setup
  • 21
    Great filtering
  • 16
    Live logging
  • 15
    Json log support
  • 10
    Log Management
  • 10
    Alerting
  • 7
    Great Dashboards
  • 7
    Love the product
  • 4
    Heroku Add-on
  • 2
    Easy to setup and use
  • 2
    Easy setup
  • 2
    No alerts in free plan
  • 2
    Great UI
  • 2
    Good parsing
  • 2
    Powerful
  • 2
    Fast search
  • 2
    Backup to S3

Sign up to add or upvote prosMake informed product decisions

Cons of Elasticsearch
Cons of Loggly
  • 7
    Resource hungry
  • 6
    Diffecult to get started
  • 5
    Expensive
  • 4
    Hard to keep stable at large scale
  • 3
    Pricey after free plan

Sign up to add or upvote consMake informed product decisions

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 Loggly?

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

Need advice about which tool to choose?Ask the StackShare community!

Jobs that mention Elasticsearch and Loggly as a desired skillset
What companies use Elasticsearch?
What companies use Loggly?
See which teams inside your own company are using Elasticsearch or Loggly.
Sign up for StackShare EnterpriseLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Elasticsearch?
What tools integrate with Loggly?

Sign up to get full access to all the tool integrationsMake informed product decisions

Blog Posts

May 21 2019 at 12:20AM

Elastic

ElasticsearchKibanaLogstash+4
12
5185
GitHubPythonReact+42
49
40767
GitHubPythonNode.js+47
55
72368
What are some alternatives to Elasticsearch and Loggly?
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