Tiered Storage Overview
Tiered Storage is a storage optimization capability provided by Apache Doris. It tiers infrequently accessed cold data down to lower-cost storage media (HDD, object storage, HDFS) while keeping hot data on high-performance storage. This significantly reduces storage cost without sacrificing query efficiency.
Applicable Scenarios
- High storage cost pressure, where you want to reduce the cost of historical data.
- Data has clear hot and cold access patterns (for example, the last 7 days are hot data and earlier data is cold data).
- Existing object storage (S3/OSS/COS, etc.) or HDFS resources can be reused.
- Different deployment modes (integrated storage-compute or storage-compute separation) require different cold data storage solutions.
Quick Decision
Choose the appropriate tiering mode based on your deployment conditions and cost goals:
| User Scenario | Recommended Mode | Key Benefit |
|---|---|---|
| Have the conditions for storage-compute separation deployment and pursue extreme elastic scaling | Storage-Compute Separation | Single-replica storage with independent scaling of compute and storage |
| Integrated storage-compute mode, want to optimize local SSD resources | Local Tiering | Cool down cold data from SSD to HDD, saving high-performance storage |
| Integrated storage-compute mode, want to use object storage or HDFS to reduce cost | Remote Tiering | Store cold data as a single replica on object storage or HDFS for deep cost reduction |
Three Tiering Modes Explained
Doris provides three cold data tiering solutions for different deployment conditions. You can choose flexibly based on your actual situation.
Mode Comparison Table
The following table summarizes the applicable conditions and core characteristics of the three modes for quick comparison:
| Cold Data Option | Applicable Condition | Core Characteristics |
|---|---|---|
| Storage-Compute Separation | You have the conditions to deploy storage-compute separation | - Data is fully stored in object storage as a single replica - Local cache accelerates hot data access - Storage and compute resources scale independently, significantly reducing storage cost |
| Local Tiering | In integrated storage-compute mode, you want to further optimize local storage resources | - Supports cooling cold data from SSD to HDD - Fully uses the local storage hierarchy to save high-performance storage cost |
| Remote Tiering | In integrated storage-compute mode, use cheap object storage or HDFS to further reduce cost | - Cold data is saved as a single replica to object storage or HDFS - Hot data continues to use local storage - Cannot be used together with local tiering on the same table |
1. Storage-Compute Separation
Applicable scenario: You have the conditions to deploy storage-compute separation and pursue elastic scaling and extreme cost reduction.
Core characteristics:
- Data is fully stored in object storage as a single replica.
- Local cache accelerates hot data access.
- Storage and compute resources scale independently, significantly reducing storage cost.
2. Local Tiering
Applicable scenario: In integrated storage-compute mode, you want to further optimize local storage resources.
Core characteristics:
- Supports cooling cold data from SSD to HDD.
- Fully uses the local storage hierarchy to save high-performance storage cost.
For detailed configuration and usage, see Local Disk Tiered Storage.
3. Remote Tiering
Applicable scenario: In integrated storage-compute mode, use cheap object storage or HDFS to further reduce cost.
Core characteristics:
- Cold data is saved as a single replica to object storage or HDFS.
- Hot data continues to use local storage.
- Cannot be used together with local tiering on the same table.
For detailed configuration and usage, see Local-Remote Tiered Storage.
Design Goals
Through the three modes above, Doris flexibly adapts to user deployment conditions and achieves the following goals:
- Balance between query efficiency and storage cost: Hot data keeps high-performance access, while cold data benefits from low-cost storage.
- Flexible adaptation to multiple deployment forms: Compatible with both integrated storage-compute and storage-compute separation modes.
- Reuse of existing infrastructure: Supports object storage, HDFS, local HDD, and other cold storage media.
FAQ
Q1: What is the essential difference between storage-compute separation and remote tiering?
- Storage-Compute Separation: All data (including hot data) is stored as a single replica in object storage, with the local disk used only as a cache for acceleration.
- Remote Tiering: Only cold data is tiered down to object storage/HDFS, while hot data remains on local storage. It is an optimization within the integrated storage-compute architecture.
Q2: Can local tiering and remote tiering be used at the same time?
No. The same table cannot mix local tiering with remote tiering.
Q3: How do I decide which mode to choose?
- If you have the conditions to deploy storage-compute separation, prefer storage-compute separation.
- If you use integrated storage-compute and only want to optimize local disk cost, choose local tiering.
- If you use integrated storage-compute and want to use object storage or HDFS to reduce cost, choose remote tiering.
Q4: Does cold data tiering affect query performance?
Cold data queries may be slightly slower because of the performance differences between media (HDD/object storage have higher latency than SSD), but Doris minimizes the performance loss through mechanisms such as local cache.
