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Overview
The article discusses Facebook's use of Apache Hadoop in its data warehouse, detailing three primary applications: web analytics, distributed database storage, and MySQL database backups. It highlights enhancements made by Facebook's engineering team, including open-sourcing specific versions of Hadoop and contributing features back to the community.
What You'll Learn
1
How to enhance Apache Hadoop for low-latency applications
2
Why HDFS-RAID can save storage costs in large datasets
3
When to use Apache HBase for reliable data storage
Prerequisites & Requirements
- Understanding of distributed systems and data warehousing concepts
- Familiarity with Apache Hadoop and its ecosystem(optional)
Key Questions Answered
What are the three main uses of Apache Hadoop at Facebook?
Facebook utilizes Apache Hadoop for web analytics, as a distributed database, and for MySQL database backups. Each application serves different needs, such as batch processing for analytics and real-time data collection for applications like Messages.
How does Facebook enhance Apache Hadoop's performance?
Facebook has made several enhancements to Apache Hadoop, including improving the NameNode's locking model for scalability and implementing the AvatarNode for high availability. These changes allow Hadoop to handle large datasets and maintain uptime during updates.
What is the purpose of the AvatarNode in Hadoop?
The AvatarNode is a high availability feature that allows for an always-up Hadoop Distributed File System (HDFS). It consists of a primary and a standby node, coordinated via Apache ZooKeeper, enabling software deployment without downtime.
What challenges is Facebook facing with its Hadoop clusters?
Facebook is encountering physical limitations in data centers that restrict the size of single clusters. They are working on solutions to enable Hadoop functionality across multiple data centers and improve hardware utilization in heterogeneous environments.
Key Statistics & Figures
Size of the largest Hadoop cluster
30 petabytes
This is possibly the largest single Hadoop cluster in the world, showcasing Facebook's scale in data processing.
Daily data scanned in the warehouse
2 petabytes
This volume indicates the high throughput capabilities of Facebook's data warehousing system.
Number of machines in the largest warehouse cluster
3000
This demonstrates the extensive infrastructure Facebook has built to support its data processing needs.
Technologies & Tools
Backend
Apache Hadoop
Used for data warehousing, distributed database storage, and MySQL backups.
Backend
Apache Hbase
Used for storing data in Hadoop clusters, particularly for real-time applications.
Backend
Apache Zookeeper
Coordinates the AvatarNodes in Hadoop for high availability.
Storage
Hadoop Distributed File System
Provides storage for data processed by Hadoop.
Backend
Scribe
Facilitates the collection of click logs and streaming data into Hadoop clusters.
Key Actionable Insights
1Consider implementing HDFS-RAID to reduce storage costs in large-scale data environments.HDFS-RAID allows for fewer copies of data while maintaining the same data loss probability, which can significantly save storage space in environments handling petabytes of data.
2Utilize the AvatarNode feature to enhance the availability of your Hadoop clusters.By deploying AvatarNodes, you can ensure that your HDFS remains operational during software updates, minimizing downtime and maintaining service continuity.
3Leverage Apache HBase for applications requiring reliable data storage and low-latency access.HBase's integration with Hadoop allows for efficient data handling in real-time applications, making it suitable for use cases like click log collection.
Common Pitfalls
1
Failing to account for the physical limitations of data centers can hinder cluster scalability.
As clusters grow, physical space and resource limitations can become significant bottlenecks. It's essential to plan for scalability across multiple data centers to avoid these issues.
Related Concepts
Distributed Databases
Data Warehousing Best Practices
High Availability In Distributed Systems