How Apache Pinot Leverages Data Sketches
Apache Pinot is a great choice of real-time analytics platform for organizations looking to process and analyze large datasets in real-time more efficiently. Data sketches are a capability that allows Pinot to enhance query speed and performance. In this blog, I’ll introduce the concept of data sketches, how they’re used in Apache Pinot, and the benefits of data sketches for Pinot users.
What are data sketches?
Data sketches are compact data structures crucial in optimizing query performance and enhancing analytical capabilities in various data processing systems. The term "sketch" is commonly used to describe algorithms and associated data structures that implement theoretical concepts. In this context, "sketch" alludes to an artist's sketch, emphasizing the idea of a simplified representation or approximation of complex data.
These sketches provide approximate answers to queries with high accuracy while minimizing memory usage and computational overhead. By leveraging data sketches, organizations can efficiently estimate key metrics such as distinct counts, percentiles, and frequencies, enabling quick insights into large datasets without requiring exhaustive computations.
Data sketches serve as powerful tools for processing and analyzing large datasets efficiently. These compact data structures offer a lightweight and scalable solution for estimating metrics and summarizing data distributions. By utilizing data sketches, applications can enhance query performance, improve analytical capabilities, and derive valuable insights from their data quickly and accurately.
Data sketches and Apache Pinot
Apache Pinot leverages data sketches to optimize query performance and enhance analytical capabilities. By incorporating data sketches into its functions and features, Apache Pinot can efficiently estimate metrics like distinct counts, percentiles, and frequencies with high accuracy and minimal computational cost. These sketches enable Apache Pinot to provide fast and accurate results for analytical queries, making it a valuable tool for processing and analyzing large datasets in real-time.
In Apache Pinot, data sketches are utilized in various functions, such as:
Distinct count estimation
Percentile calculation
Frequency estimation
Multi-value support
Leveraging data sketch functions in Apache Pinot is beneficial when you need to efficiently process and analyze large datasets while maintaining good query performance. Some specific situations where you would need to leverage data sketch functions in Apache Pinot include:
Handling real-time data streams: Data sketch functions as ingestion transforms to precompute some hashes, so that the queries can run faster to calculate final sketch results.
Reducing memory usage: Data sketches allow for compact representations of data, reducing memory requirements while still providing valuable insights, which is essential when dealing with large datasets.
Improving query performance: Data sketch functions can be used for operations like distinct counting and frequent value estimation to improve query performance and speed up data analysis tasks.
Scaling data analytics: Data sketch functions in Apache Pinot enable efficient processing of large-scale data analytics tasks, making it easier to handle massive amounts of data and derive meaningful insights. In addition, sketch functions can also help scale to higher query rate, with less need for memory and being able to compute faster
By leveraging data sketches, Apache Pinot can approximate key metrics efficiently, leading to faster query responses and improved performance. Integrating data sketches in Apache Pinot enhances its analytical capabilities, enabling users to gain valuable insights from their data and make informed decisions based on accurate estimations of metrics.
Pinot Functions that use Data Sketch
Apache Pinot offers various functions that utilize data sketches to efficiently process and analyze data. These functions include operations like distinct counting, frequent value estimation, and probabilistic data structures. By leveraging data sketches, Apache Pinot can provide approximate results with reduced memory usage and improved query performance, making it a powerful tool for handling large-scale data analytics tasks. These functions include:
FrequentLongsSketch:
Description: Estimates the frequency of long values in a dataset efficiently.
FrequentStringsSketch:
Description: Estimates the frequency of string values in a dataset.
DISTINCTCOUNTRAWHLL:
Description: Calculates the distinct count of raw values using the HyperLogLog algorithm.
DISTINCTCOUNTRAWHLLMV:
Description: Calculates the distinct count of raw values using the HyperLogLog algorithm with multi-values support.
DISTINCTCOUNTRAWTHETASKETCH:
Description: Estimates the distinct count of raw values using the Theta sketch algorithm.
DISTINCTCOUNTTHETASKETCH:
Description: Estimates the distinct count of values using the Theta sketch algorithm.
These data sketch functions in Apache Pinot play a crucial role in optimizing query performance and enabling efficient estimation of metrics for analytical queries.
HyperLogLog
As an example, let's look at one of these sketches: HyperLogLog (HLL). I like this one because of its name.
The HyperLogLog (HLL) sketch is a probabilistic data structure used for estimating the cardinality of a set (or how many unique values exist in a set). It hashes to make accurate estimations without having to check every single item. The HLL sketch can adjust its size as needed and is easy to update by adding new items. It gives us a pretty good estimate of the number of unique items in a large group, especially when there are a lot of items.
Here is an example of a Pinot function that uses HLL sketch: DISTINCTCOUNTHLLPLUS:
SELECT DISTINCTCOUNTHLLPLUS(column_name)
FROM table_name;
You would use the DISTINCTCOUNTHLLPLUS function when calculating the approximate count of distinct values in a column using the HyperLogLog++ algorithm. HLL++ has higher accuracy than HLL when dimension cardinality is at 10k-100k.
Summary
Overall, leveraging data sketch functions in Apache Pinot is advantageous when optimizing query performance, reducing memory usage, handling real-time data streams, and scaling data analytics operations effectively.
Apache Pinot's data sketch functions offer a powerful way to summarize and analyze large datasets efficiently. By providing approximate results with minimal memory usage, these functions enable quick insights into data trends and patterns. Their advantages include faster query processing, reduced resource requirements, and effective handling real-time data streams. Overall, leveraging Apache Pinot's data sketch functions can significantly enhance data analysis capabilities and streamline decision-making processes.