Quickwit vs. Elasticsearch: Which Tool To Choose? [2025 Guide]

Data indexing and search are essential for quickly retrieving relevant information from large datasets. They help improve efficiency, save time, and support better decision-making by making data easily accessible.

Elasticsearch has been a popular choice for data indexing and search, but newer tools like Quickwit now offer alternatives for specific needs. The right choice depends on performance, scalability, cost, and how well it fits your use case.

This blog explores the features, pros, and cons of Quickwit and Elasticsearch to help you determine which is better suited for your needs.

Lets get started!

Table of Contents:

1. Quickwit vs Elasticsearch: Feature Comparison
2. Overview of Quickwit and Elasticsearch
3. Architecture Comparison
4. Performance
5. Scalability
6. Cost Efficiency
7. Ease of Use and Deployment
8. Community and Ecosystem
9. Security and Compliance
10. Pros and Cons
11. Choosing the Right Solution
12. When to Choose Quickwit
13. When to Choose Elasticsearch

Quickwit vs Elasticsearch: Feature Comparison

Overview of Quickwit and Elasticsearch

What is Quickwit?

Quickwit is a modern, high-performance, and lightweight search engine built specifically for log management and analytics. It is an open-source solution that focuses on distributed search and utilizes object storage for scalability. Designed to be simple yet powerful, Quickwit is a promising alternative for applications that demand low-latency searches on large-scale datasets.

Key Features of Quickwit

• Optimized for log and event data.
• Distributed search architecture.
• Uses object storage (e.g., AWS S3, Google Cloud Storage) for scalability.
• Lightweight, with a small operational footprint.
• Schema-on-write for flexible indexing.
• Built-in support for time-based retention policies.

What is Elasticsearch?

Elasticsearch, part of the Elastic Stack (ELK Stack), is a widely used search and analytics engine known for its versatility. Built on Apache Lucene, Elasticsearch provides robust capabilities for full-text search, structured search, and analytics. It powers many use cases, including application search, log analytics, and real-time monitoring.

Key Features of Elasticsearch

• Advanced full-text search capabilities.
• Extensive support for structured and unstructured data.
• Rich ecosystem, including Kibana for visualization.
• Scalability through distributed indexing and searching.
• High flexibility for a variety of use cases beyond logs.
• Plugins and integrations for diverse workflows.

Architecture Comparison

Quickwit

Quickwit’s architecture is designed with simplicity and efficiency in mind. It adopts a distributed search model and stores indexed data in object storage, such as AWS S3 or other compatible storage solutions. This approach significantly reduces the cost and complexity of managing data storage.

• Object Storage First: By leveraging object storage, Quickwit minimizes storage costs and allows for virtually unlimited scaling.
• Index Segments: Quickwit uses immutable index segments, enabling faster indexing and efficient querying.
• Streamlined Deployment: Lightweight and easy to deploy, requiring fewer resources.

Elasticsearch

Elasticsearch relies on a cluster-based architecture, where data is distributed across multiple nodes for scalability and fault tolerance. Each node has specific responsibilities, such as master nodes for cluster management and data nodes for indexing and searching.

• Shard-based Distribution: Elasticsearch splits data into shards, distributing them across nodes to ensure high availability.
• Replication: Provides data redundancy and reliability through replication.
• Cluster Management: Offers dynamic scaling but requires careful configuration and resource planning.
• On-disk Indexing: Stores data locally on disk, leading to higher infrastructure requirements.

Performance

Quickwit

Quickwit is optimized for high-speed log ingestion and search queries on large datasets. Its performance benefits come from the use of:

• Immutable Segments: Faster indexing and retrieval.
• Object Storage: Eliminates the overhead of managing traditional block storage.
• Time-based Optimization: Designed for time-series data, making log analytics more efficient.

Use Cases Suited for Quickwit:

• Log management.
• Real-time monitoring of event data.
• Applications with high write throughput and frequent queries.

Elasticsearch

Elasticsearch excels in full-text search and complex querying scenarios. It supports advanced features like fuzzy search, autocomplete, and aggregations, making it suitable for a wide variety of applications.

However, Elasticsearch’s performance can degrade with large-scale datasets, particularly when managing high volumes of write operations or when cluster configurations are not optimized.

Use Cases Suited for Elasticsearch:

• Application search (e.g., e-commerce platforms).
• Complex analytics dashboards.
• Full-text search on unstructured data.

Scalability

Quickwit

Quickwit’s reliance on object storage ensures cost-effective scalability. It can handle petabytes of data with ease, and adding more storage does not require significant architectural changes. Moreover, its lightweight nature means it can scale horizontally without much operational overhead.

Scalability Pros:

• Cost-efficient storage.
• Simple scaling using object storage.
• No need for high-memory or high-CPU nodes.

Scalability Cons:

• Limited to use cases involving log or time-series data.

Elasticsearch

Elasticsearch is highly scalable but requires careful planning and resource allocation. Scaling Elasticsearch involves adding nodes to a cluster and rebalancing shards, which can be complex and resource-intensive.

Scalability Pros:

• Flexible scaling options.
• Supports diverse use cases across industries.

Scalability Cons:

• High storage and operational costs.
• Requires careful shard and cluster management.

Cost Efficiency

Quickwit

Quickwit’s focus on object storage makes it significantly cheaper to operate compared to Elasticsearch. Object storage, such as S3, provides low-cost, durable storage for massive datasets, while Quickwit’s lightweight architecture reduces computational costs.

Cost Benefits:

• Lower storage costs due to object storage.
• Reduced infrastructure requirements.

Elasticsearch

Elasticsearch can become expensive as data volume grows. It relies on local storage and requires powerful nodes for optimal performance, driving up infrastructure costs. Additionally, cluster management and scaling efforts can increase operational expenses.

Cost Challenges:

• Expensive storage due to local disk reliance.
• High operational overhead for large clusters.

Ease of Use and Deployment

Quickwit

Quickwit’s simplicity makes it easy to deploy and manage. It has a lightweight footprint, and its use of object storage minimizes the need for extensive cluster configurations. The setup process is straightforward, and the learning curve is minimal.

Elasticsearch

Elasticsearch provides extensive documentation and a rich ecosystem, but its complexity can be daunting for beginners. Cluster management, shard allocation, and scaling require significant expertise, and deploying Elasticsearch at scale often involves dedicated infrastructure teams.

Community and Ecosystem

Quickwit

Quickwit is a newer project with a smaller but growing community. While it lacks the extensive ecosystem of plugins and integrations that Elasticsearch offers, it focuses on solving specific problems in log management and analytics efficiently.

Elasticsearch

Elasticsearch has a well-established community and ecosystem. It integrates seamlessly with tools like Kibana, Logstash, and Beats, making it a go-to choice for many organizations. The abundance of plugins and third-party integrations further enhances its versatility.

Security and Compliance

Quickwit

Quickwit supports authentication and role-based access control (RBAC) for managing access to data. However, its security features are still evolving compared to Elasticsearch.

Elasticsearch

Elasticsearch provides robust security features, including encryption, RBAC, and auditing. However, many advanced features are part of the paid Elastic Stack subscription, which can increase costs.

Pros and Cons

Quickwit

Pros:

• Cost-efficient due to object storage.
• Optimized for log analytics and time-series data.
• Simple and lightweight deployment.

Cons:

• Limited ecosystem and integrations.
• Focused primarily on log management, not general-purpose search.

Elasticsearch

Pros:

• Extensive ecosystem with powerful features.
• Suitable for diverse use cases beyond logs.
• Advanced query capabilities.

Cons:

• High infrastructure and operational costs.
• Complex to deploy and scale.

Choosing the Right Solution

Quickwit Example: Storing and Retrieving Data

Create an index:

quickwit index create --index-uri s3://my-bucket/quickwit-logs --index-config /path/to/config.toml

Add data:

quickwit index ingest --index-uri s3://my-bucket/quickwit-logs --input-path /path/to/logs.json

Retrieve data:

quickwit search --index-uri s3://my-bucket/quickwit-logs --query "error AND timestamp:[2023-01-01 TO 2023-12-31]"

Elasticsearch Example: Storing and Retrieving Data

Create an index:

curl -X PUT "http://localhost:9200/logs" -H 'Content-Type: application/json' -d'
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 1
  },
  "mappings": {
    "properties": {
      "timestamp": { "type": "date" },
      "message": { "type": "text" },
      "level": { "type": "keyword" }
    }
  }
}'

Add data:

curl -X POST "http://localhost:9200/logs/_doc/" -H 'Content-Type: application/json' -d'
{
  "timestamp": "2023-05-15T12:34:56",
  "message": "An error occurred.",
  "level": "error"
}'

Retrieve data:

curl -X GET "http://localhost:9200/logs/_search" -H 'Content-Type: application/json' -d'
{
  "query": {
    "bool": {
      "must": [
        { "match": { "level": "error" } },
        { "range": { "timestamp": { "gte": "2023-01-01", "lte": "2023-12-31" } } }
      ]
    }
  }
}'

By including these examples, you can better understand the practical applications of Quickwit and Elasticsearch for your data indexing and querying needs.

When to Choose Quickwit

• You need a lightweight, cost-efficient solution for log management.
• Your data is primarily time-series or event-based.
• You want to minimize operational overhead by using object storage.

When to Choose Elasticsearch

• You require advanced full-text search and analytics capabilities.
• Your use case spans multiple data types and complex queries.
• You need a mature ecosystem with visualization tools like Kibana.

Conclusion

Quickwit and Elasticsearch cater to different needs and use cases. Quickwit is an excellent choice for organizations focused on log management and time-series data, offering simplicity and cost efficiency. Elasticsearch, on the other hand, remains a versatile powerhouse for full-text search and analytics, suitable for diverse applications but at a higher operational cost.

Ultimately, the decision depends on your specific requirements, budget, and technical expertise. Both solutions are powerful in their own right, and understanding their strengths and limitations will help you make an informed choice for your organization.