Penyimpanan Data Dalam Memori

Introduction

In-memory data stores are databases that store data in memory rather than on disk. This allows for faster access to data and higher performance than traditional disk-based databases. In-memory data stores, such as real-time analytics, caching, and session management, are often used in applications that require high performance and low latency.

Key Characteristics

Data stored in memory

High performance and low latency

No disk I/O

Limited storage capacity

Volatile data storage

CAP Theorem

General CAP theorem handling

In-memory data stores are designed to prioritize consistency and partition tolerance over availability, making them a good fit for applications that require high performance and low latency.

Guarantee of consistency

In-memory data stores typically provide strong consistency, ensuring that all nodes see the same data simultaneously.

Guarantee of availability

In-memory data stores are designed to prioritize consistency and partition tolerance over availability, which means that they may not be able to continue operating if some nodes in the system fail.

Guarantee of partition tolerance

In-memory data stores are designed to be highly scalable and handle large amounts of data across multiple nodes. They use automatic sharding and replication to ensure that data is distributed evenly across the system and that it can continue operating even if some nodes fail.

Usage

Best usage

In-memory data stores are well-suited for applications that require high performance and low latency, such as real-time analytics, caching, and session management.

Neutral usage

In-memory data stores can also be used for applications that require persistent storage but may not be the best choice for applications that require high storage capacity or durability.

Worst usage

In-memory data stores may not be the best choice for applications that require high storage capacity or durability or for applications that require strict availability guarantees.

System Design Role

In-memory data stores are well-suited for systems that require high performance and low latency, such as distributed systems and cloud-based applications.

Data Model

Key-value data model

Non-relational database

Advantages: high performance and low latency, good for real-time analytics and caching

Disadvantages: limited storage capacity, volatile data storage

Query Language

NoSQL query language (e.g., Redis commands)

Advantages: simple, easy to use, good performance with key-value data

Disadvantages: it may not be as powerful as SQL for complex queries

Scalability

How to make it performant

Indexing and other performance optimization techniques can make in-memory data stores performant.

High traffic handling

In-memory data stores are well-suited for high-read and high-write workloads but may require additional data partitioning and replication considerations.

How to scale it

In-memory data stores can be scaled horizontally through automatic sharding and replication.

Usage in distributed systems

In-memory data stores can be used in distributed systems but may require additional data partitioning and replication considerations.

Replication

In-memory data stores typically use automatic replication to ensure data availability and durability. Best practices for replication include using a replication factor of at least three and ensuring that replicas are distributed across multiple data centers.

Best Practices

Use indexing and other performance optimization techniques to improve query performance.

Use a replication factor of at least three to ensure data availability and durability.

Monitor the system for performance issues and adjust as necessary

Common Pitfalls

Not understanding the data model and how it affects query performance

Not properly configuring replication and sharding

Not monitoring the system for performance issues

Examples

Redis

Memcached

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