Overview
The article discusses the entry of Pinot into the Apache Incubator, highlighting its capabilities as a scalable distributed OLAP data store developed at LinkedIn. It emphasizes Pinot's real-time analytics features and its significant role in various applications within LinkedIn.
What You'll Learn
1
How to utilize Pinot for real-time analytics in applications
2
Why Pinot is effective for low latency data processing
3
When to implement smart routing strategies in query execution
Prerequisites & Requirements
- Understanding of OLAP data stores and real-time analytics
- Familiarity with Apache Kafka and Hadoop(optional)
Key Questions Answered
What is Pinot and what are its main features?
Pinot is a scalable distributed OLAP data store designed for real-time analytics, capable of handling thousands of queries per second with low latency. It supports various applications at LinkedIn, including customer-facing features and internal analytics dashboards.
How does Pinot handle data ingestion?
Pinot supports near real-time data ingestion by reading events directly from streams like Kafka and from offline systems such as Hadoop. This capability allows it to ingest millions of events per second, making it suitable for dynamic data environments.
What improvements have been made to Pinot's architecture?
Recent enhancements include the introduction of a Pinot filesystem abstraction for deep storage, smart routing strategies to reduce query latency, and pluggable real-time streams that allow for ingestion from various pub-sub systems beyond Kafka.
Key Statistics & Figures
Queries per second (QPS)
1,000s
Pinot can sustain thousands of queries per second while delivering results in tens to hundreds of milliseconds.
Data ingestion rate
millions of events per second
Pinot's architecture allows it to ingest new data at a very high rate, supporting dynamic and large-scale applications.
Technologies & Tools
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Backend
Apache Pinot
Used as a distributed OLAP data store for real-time analytics.
Streaming
Apache Kafka
Serves as a source for real-time data ingestion into Pinot.
Storage
Hadoop
Used for offline data pushes to Pinot.
Key Actionable Insights
1Integrate Pinot into your analytics stack to leverage its real-time data processing capabilities.Pinot's ability to handle high query volumes with low latency makes it ideal for applications requiring immediate insights, such as user interaction tracking or business intelligence dashboards.
2Utilize Pinot's smart routing strategies to optimize query performance.By limiting server fan-out during query execution, you can significantly reduce latency, which is crucial for applications that demand quick response times.
Common Pitfalls
1
Failing to optimize query routing can lead to high latency in query responses.
Without implementing smart routing strategies, Pinot may experience longer response times due to unnecessary server fan-out during query execution.
Related Concepts
Olap Data Stores
Real-time Analytics
Data Ingestion Techniques