---
title: Understand hypercore | Tiger Data Docs
description: How Tiger Data's hybrid row-columnar engine accelerates analytics while keeping ingest fast
---

Hypercore is Tiger Data‘s hybrid row-columnar storage engine. New data lands in the rowstore for fast inserts and updates, then automatically converts to the columnstore where analytical queries read fewer bytes and run faster. This conversion includes data compression of up to 98%, which dramatically cuts storage costs.

## Why it matters

- **Fast ingest**: the rowstore handles high-throughput writes, upserts, and deletes with full ACID guarantees.
- **Fast analytics**: the columnstore enables efficient scanning and aggregation, with [chunk skipping](/docs/build/performance-optimization/improve-hypertable-performance/index.md) to skip irrelevant chunks entirely.
- **Lower storage costs**: 90-98% compression in the columnstore reduces storage without sacrificing query performance.
- **Full mutability**: data in both the rowstore and columnstore supports standard SQL `INSERT`, `UPDATE`, `DELETE`, and `UPSERT` with transactional semantics.

## How it works

Hypercore is a hybrid row-columnar storage engine in TimescaleDB. It is designed specifically for real-time analytics and powered by time-series data. The advantage of hypercore is its ability to seamlessly switch between row-oriented and column-oriented storage, delivering the best of both worlds:

![Hypercore workflow: data moves from rowstore to columnstore](/docs/_astro/hypertable-with-hypercore-enabled.Be3qa9UO_21XTLP.webp)

Hypercore solves the key challenges in real-time analytics:

- High ingest throughput
- Low-latency ingestion
- Fast query performance
- Efficient handling of data updates and late-arriving data
- Streamlined data management

Hypercore’s hybrid approach combines the benefits of row-oriented and column-oriented formats:

- **Fast ingest with rowstore**: new data is initially written to the rowstore, which is optimized for high-speed inserts and updates. This process ensures that real-time applications easily handle rapid streams of incoming data. Mutability, upserts, updates, and deletes happen seamlessly.

- **Efficient analytics with columnstore**: as the data **cools** and becomes more suited for analytics, it is automatically converted to the columnstore. This columnar format enables fast scanning and aggregation, optimizing performance for analytical workloads while also saving significant storage space.

- **Faster queries on compressed data in columnstore**: in the columnstore conversion, hypertable chunks are compressed by up to 98%, and organized for efficient, large-scale queries. Combined with [chunk skipping](/docs/build/performance-optimization/improve-hypertable-performance/index.md), this helps you save on storage costs and keeps your queries operating at lightning speed.

- **Fast modification of compressed data in columnstore**: just use SQL to add or modify data in the columnstore. TimescaleDB is optimized for superfast INSERT and UPSERT performance.

- **Full mutability with transactional semantics**: regardless of where data is stored, hypercore provides full ACID support. Like in a vanilla PostgreSQL database, inserts and updates to the rowstore and columnstore are always consistent, and available to queries as soon as they are completed.

For an in-depth explanation of how hypertables and hypercore work, see the [Data model](/docs/learn/deep-dive/whitepaper#data-model/index.md).

## Get hands on

[Set up hypercore](/docs/build/columnar-storage/setup-hypercore/index.md)

[Enable and configure columnstore on your hypertables.](/docs/build/columnar-storage/setup-hypercore/index.md)

[Basic compression with hypercore](/docs/build/how-to/basic-compression/index.md)

[Quick-start guide for enabling compression.](/docs/build/how-to/basic-compression/index.md)

[Compression methods](/docs/learn/columnar-storage/compression-methods/index.md)

[How hypercore compresses your data under the hood.](/docs/learn/columnar-storage/compression-methods/index.md)

## Learn more

- [Improve query and upsert performance](/docs/build/performance-optimization/secondary-indexes/index.md): Segmenting and ordering columnstore data.
- [Convert continuous aggregates to the columnstore](/docs/build/continuous-aggregates/compression-on-continuous-aggregates/index.md): Compress aggregated data.
- [Troubleshoot hypercore](/docs/build/tips-and-tricks/troubleshoot-hypercore/index.md): Common issues and solutions.
- [Hypercore API overview](/docs/reference/timescaledb/hypercore/index.md): All columnstore functions and policies.