About Quickwit

Quickwit is a distributed search engine optimized for querying large data sets stored in cost-efficient cloud storage, utilizing Rust and the Tantivy library for high performance and low latency. It enables rapid log and trace analysis with sub-second search capabilities, while supporting multi-tenancy and compliance with data retention policies.

<problem> Traditional search engines struggle to efficiently query large datasets stored in cost-effective cloud storage due to the high I/O overhead and computational demands of processing raw data. Existing solutions often require significant infrastructure investment and are not optimized for the low query-per-second (QPS) but high-volume nature of log and trace analysis. </problem> <solution> Quickwit is a distributed search engine designed for querying large datasets directly on cloud storage with sub-second latency. By leveraging a custom file format that minimizes I/O requests and a smart I/O scheduler that maximizes throughput, Quickwit optimizes search performance on object stores like Amazon S3, MinIO, and Ceph. Its architecture, built with Rust and the Tantivy search library, decouples compute and storage, enabling horizontal scalability and cost-effective log and trace analysis. Quickwit supports schemaless indexing, multi-tenancy, data retention policies, and targeted deletions for GDPR compliance. </solution> <features> - Optimized file format to reduce the number and size of I/O operations on cloud storage - Smart I/O scheduling to maximize throughput and minimize latency - Written in Rust for high performance, no garbage collection overhead, and vectorized processing - Powered by the Tantivy search engine library - Schemaless indexing for flexible data ingestion - Multi-tenancy support with optimized indexing and partitioning - Retention and lifecycle policies for data management - Support for infrequent, targeted deletions for GDPR compliance - REST API for integration with existing systems </features> <target_audience> Quickwit is designed for DevOps engineers, data engineers, and organizations that need to perform rapid log and trace analysis on large datasets stored in cloud storage. </target_audience>

What does Quickwit do?

Where is Quickwit located?

Quickwit is based in San Francisco, United States.

When was Quickwit founded?

Quickwit was founded in 2020.

How much funding has Quickwit raised?

Quickwit has raised 2600000.

Location

San Francisco, United States

Founded

2020

Funding

2600000

Employees

5 employees

Major Investors

FirstMark, Firstminute Capital

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Quickwit

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Executive Summary

quickwit.io 1K+

Crunchbase

Founded 2020 – San Francisco, United States

Funding

Estimated Funding

$2M+

Major Investors

FirstMark, Firstminute Capital

Team (5+)

No team information available.

Company Description

Problem

Traditional search engines struggle to efficiently query large datasets stored in cost-effective cloud storage due to the high I/O overhead and computational demands of processing raw data. Existing solutions often require significant infrastructure investment and are not optimized for the low query-per-second (QPS) but high-volume nature of log and trace analysis.

Solution

Quickwit is a distributed search engine designed for querying large datasets directly on cloud storage with sub-second latency. By leveraging a custom file format that minimizes I/O requests and a smart I/O scheduler that maximizes throughput, Quickwit optimizes search performance on object stores like Amazon S3, MinIO, and Ceph. Its architecture, built with Rust and the Tantivy search library, decouples compute and storage, enabling horizontal scalability and cost-effective log and trace analysis. Quickwit supports schemaless indexing, multi-tenancy, data retention policies, and targeted deletions for GDPR compliance.

Features

Optimized file format to reduce the number and size of I/O operations on cloud storage

Smart I/O scheduling to maximize throughput and minimize latency

Written in Rust for high performance, no garbage collection overhead, and vectorized processing

Schemaless indexing for flexible data ingestion

Multi-tenancy support with optimized indexing and partitioning

Retention and lifecycle policies for data management

Support for infrequent, targeted deletions for GDPR compliance

REST API for integration with existing systems

Target Audience

Quickwit is designed for DevOps engineers, data engineers, and organizations that need to perform rapid log and trace analysis on large datasets stored in cloud storage.