Writing data in Elastic Search? Think again.

Why Elasticsearch Should Not Be Preferred for Writing Data

Elasticsearch is one of the most popular tools for searching and analyzing data. Its ability to process massive datasets and deliver near real-time search results has made it a cornerstone for applications ranging from e-commerce platforms to monitoring systems. However, when it comes to writing data, Elasticsearch falls short in several critical areas. In this article, we’ll explore the challenges of writing data to Elasticsearch, explain why it’s not ideal for write-heavy use cases, and discuss better alternatives. ??????

Understanding Elasticsearch’s Core Strength: Read-Heavy Workloads

Elasticsearch is optimized for fast querying and analytics, powered by its inverted index architecture. This makes it a perfect choice for use cases where:

• Data is read and searched frequently.
• Queries involve complex filtering, aggregations, or full-text search.

However, Elasticsearch was not built for heavy or continuous data writing. Its architecture and design decisions, while excellent for search, introduce inefficiencies for write-intensive applications. ??????

Challenges of Writing Data to Elasticsearch

1. High Resource Consumption

• Indexing Overhead: Every time data is written to Elasticsearch, it goes through a series of processes:
• These steps consume significant CPU, memory, and disk resources. ???????
• Write Amplification: Elasticsearch continuously creates and merges segments to optimize queries. These operations amplify disk I/O, leading to slower writes and higher infrastructure costs. ??????

2. Eventual Consistency

• Elasticsearch is eventually consistent. This means that after writing data to one node, it may take time for the data to replicate across the cluster and become available for querying.
• For applications requiring immediate consistency (e.g., financial transactions or inventory updates), this delay is unacceptable. ??????

3. Frequent Updates Are Costly

• Unlike traditional databases, Elasticsearch doesn’t modify existing documents directly.
• Instead, it marks the old document as deleted and writes a new version of the document to a new segment. This process:

4. Limited Transactional Capabilities

• Elasticsearch does not support ACID (Atomicity, Consistency, Isolation, Durability) transactions.
• Concurrent writes or updates can lead to data conflicts or inconsistencies, making it unsuitable for applications requiring strict transactional guarantees. ?????

5. Performance Degradation Under Heavy Write Load

• Write-heavy workloads can overwhelm Elasticsearch, causing cluster instability. Symptoms include:

6. Disk Usage Overhead

• The inverted index and additional metadata (e.g., for replicas and segments) result in significant disk usage. Frequent writes, updates, and deletes exacerbate this problem, leading to higher storage requirements and costs. ??????

Example: Writing Data to Elasticsearch

Scenario

Imagine a real-time analytics system for tracking user interactions on a website. The system logs every page view, button click, and transaction as a separate document in Elasticsearch. With millions of interactions recorded daily, the following challenges arise: ??????

1. High Write Throughput:
2. Frequent Updates:
3. Delayed Availability:

Better Alternatives for Write-Heavy Workloads

If your application involves high write throughput or frequent updates, consider these alternatives: ??????

1. Relational Databases

• Examples: MySQL, PostgreSQL
• Advantages:
• Use Cases: Financial systems, inventory management, and applications requiring strong consistency.

2. NoSQL Databases

• Examples: MongoDB, Apache Cassandra, DynamoDB
• Advantages:
• Use Cases: Real-time analytics, distributed systems, and high-availability applications.

3. Message Queues or Streaming Systems

• Examples: Apache Kafka, Amazon Kinesis
• Advantages:
• Use Cases: Event logging, real-time data pipelines, and buffering writes before processing.

4. Time-Series Databases

• Examples: InfluxDB, TimescaleDB
• Advantages:
• Use Cases: IoT data, monitoring systems, and performance analytics. ??????

Optimizing Writes to Elasticsearch (If You Must)

For scenarios where Elasticsearch is necessary for search and analytics but still requires frequent data writes, consider the following optimizations: ??????

1. Use Bulk API:
2. Adjust Refresh Interval:
3. Shard Configuration:
4. Pre-process Data:
5. Monitor and Scale:

Conclusion

Elasticsearch is an exceptional tool for search and analytics, but it is not designed to handle write-heavy workloads efficiently. Its resource-intensive indexing process, eventual consistency model, and limited transactional capabilities make it unsuitable for applications that prioritize high write throughput or frequent updates. ?????

Instead, consider using purpose-built databases and systems for writing data, and use Elasticsearch as a secondary layer for search and analytics. This approach ensures better performance, scalability, and cost-efficiency for your application. ??????