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Highlights

Updated on 2025-01-03 GMT+08:00

Cloud-native GeminiDB is a key-value (KV) database service featuring high stability, cost-effectiveness, elasticity, and easy O&M. It is fully compatible with the Redis protocol, supports advanced functions such as PITR recoveries for game rollback and FastLoad for feature data import, and it allows you to set the field expiration time for hash keys and blacklist for high-risk keys.

GeminiDB is widely used in scenarios such as game friends list and player rankings, ad placement, personalized recommendations, e-commerce inventory, IoV data storage, and ERP systems. For details, see Application Scenarios.

GeminiDB has the following advantages over open-source on-premises KV databases (such as Redis and Pika databases):

Table 1 Comparison between GeminiDB and open-source on-premises KV databases

Dimension

Item

Open-Source On-Premises KV Database

GeminiDB

Stability

Performance jitter caused by forks

Service stability is severely affected by fork issues.

When RDB backups are generated, the Append Only File (AOF) is rewritten, or full data is synchronized, a fork is called. This increases latency and causes out of memory (OOM) issues.

Service stability is improved as fork issues are addressed.

There is no performance jitter during backup and synchronization.

Long latency in big key scenarios

The single-thread architecture slows down subsequent requests.

In a single-thread architecture, big key requests slow down all subsequent requests and may trigger flow control or OOM issues on shards.

The multi-thread architecture reduces the impact on subsequent keys.

GeminiDB uses a multi-thread architecture, which improves concurrency and reduces the impact of big keys on subsequent read and write operations of other keys.

Bandwidth limiting during peak hours

Flow control is easily triggered, affecting services.

Open-source on-premises databases typically use a hybrid deployment that strictly limits the bandwidth. Flow control is easily triggered for smaller instances.

Up to 10 Gbit/s is supported, allowing GeminiDB to handle service surges.

By using an independent container deployment, GeminiDB can enable a load balancer to support a bandwidth of 10 Gbit/s.

Impact of scale-out on services

Scale-out can take several minutes or sometimes even hours, greatly affecting services.

Adding nodes involves data migration. Services may be affected for a few minutes or up to several hours.

Smooth scale-out is supported and has minimal impact on services.

Scale-out can be completed in seconds and without interrupting services.

Adding nodes does not requires any data migration. There is just a few seconds of jitter.

HA scenarios such as node breakdowns and primary/secondary switchovers

Long switchover time: RTO > 30s

Second-level jitters, RTO < 10s

Performance

QPS

QPS per shard: 80,000 to 100,000

In a single-thread architecture, the QPS of a single shard does not increase after CPUs are added.

QPS per shard: 10,000 to 300,000

In a multi-thread architecture, the QPS can increase linearly as CPUs are added.

Latency

Low latency

Low latency

In most service scenarios, the average latency is 1 ms, and the p99 latency is about 2 ms.

O&M capabilities

Audit logs of risky operations

Not supported

High-risk commands can be traced.

Circuit breakers triggered by abnormal requests to keys

Not supported

Key blacklists and one-click circuit breakers for high-risk operations are supported, so the entire instance is not affected.

Slow query logs

Supported

Supported. More details can be found in the logs.

Big key diagnosis

Not supported

Online diagnosis of big keys by category is supported.

Hot key diagnosis

Supported

Online diagnosis of hot keys is supported.

Cost

Utilization cost

The in-memory storage is expensive.

The cost of databases with the same specifications is 30% lower than open-source on-premises databases.

Users can purchase additional compute resources and storage resources independently to eliminate the resource waste associated with coupled storage and compute.

Data compression

Not supported

The compression ratio (4:1) enables databases with the same specifications to store more data.

Scale-out

Coupled storage and compute increases costs exponentially.

Decoupled storage and compute supports independent scaling of compute and storage resources.

Availability

/

If any pair of primary and standby nodes is faulty, the entire cluster becomes unavailable.

GeminiDB provides superlative fault tolerance (N-1 reliability).

Data reliability

/

Weak

Thousands or tens of thousands of records will be lost if nodes are restarted and the network fluctuates. Weak data consistency may cause dirty reads.

High reliability

GeminiDB provides three-copy storage, so it can serve as the primary database to replace the traditional DB+Cache solution, and it also ensures strong data consistency and avoids dirty reads.

Advanced features

Autoscaling

Not supported

Supported

Setting the expiration time for fields in hashes

Not supported

Supported. Service design is less complex and concurrency is increased.

Fast data loading

Not supported

FastLoad allows feature data to be imported faster, reducing the impact on online services.

Point-In-Time Recovery (PITR)

Not supported

Supported

PITR rollbacks and quick data restoration to the original instance are supported, making GeminiDB a great fit for gaming applications.

DR instances

Not supported

Intra-region and cross-region DR instances can be created.

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