In the world of real-time data processing and analytics, two notable technologies have emerged: Timeplus and ClickHouse Materialized Views. While both aim to provide efficient solutions for handling large volumes of data, they have distinct approaches and capabilities.
The following will explore the similarities and differences between these two technologies, highlighting their strengths and limitations, and demonstrating how they can work together to create a powerful real-time data processing pipeline.
ClickHouse Materialized Views
ClickHouse, an open-source column-oriented database management system, offers Materialized Views as a feature to pre-aggregate and transform data for faster query performance. Materialized Views in ClickHouse are essentially stored query results that are automatically updated when the underlying data changes.
STRENGTHS
Fast query performance for pre-aggregated data
Automatic updates when source data changes
Integration with ClickHouse's powerful querying capabilities
LIMITATIONS
Limited real-time processing capabilities
Potential for increased storage usage with raw input tables
Complexity in managing consistency and error handling with multiple materialized views off the back of a single input table
Possible query performance degradation during view updates, especially during higher fan-out
Support for only one input table per materialized view
Updates triggered only on data insertion to the input table
Poor performance with frequent writes of small batches
Lack of support for UNION and JOINs in view definitions
The last four points are particularly significant limitations:
Single Input Table
ClickHouse Materialized Views can only operate on a single input table. This restricts the ability to create views that combine or correlate data from multiple sources, limiting their usefulness in complex data environments.
Insert-Only Updates
Materialized Views in ClickHouse are updated only when new data is inserted into the input table. This means that updates or deletions in the source data are not reflected in the view, potentially leading to inconsistencies.
Performance Issues with Small, Frequent Writes
In scenarios where data is written in small batches but at high frequency, the constant updating of materialized views can lead to performance degradation. This is particularly problematic in real-time streaming scenarios where data arrives continuously in small increments.
Limited JOIN and UNION Capabilities
ClickHouse Materialized Views do not support UNION operations or complex JOINs. This severely limits the ability to create denormalized records or to combine data from multiple sources, which is often necessary in real-world analytics scenarios.
Enter Timeplus
Real-Time Stream Processing
Timeplus is a modern stream processing platform designed to handle real-time data with low latency supporting native streams as well as data from popular streaming platforms such as Apache Kafka and Apache Pulsar. It excels in processing, joining, and preparing streaming data before it reaches the final storage system.
STRENGTHS:
True real-time processing capabilities
Efficient handling of streaming data and materialized views
Flexible data transformation and enrichment
Low-latency data preparation
Support for complex data correlations, joins, aggregations
Ability to handle high-frequency, small-batch data writes while batching efficiently for high throughput to downstream systems (like ClickHouse)
Supports ad-hoc the joined streams within Timeplus itself to serve most recent data by operational applications and BI Tools
Key Features:
Stream-to-Stream Joins
Complex Event Processing
Time-Based Windowing and Aggregations
Dynamic Schema Adaptation
Support for UNION and Complex JOIN Operations
How Timeplus Augments ClickHouse:
Real-Time Data Preparation
Timeplus can process, join, and enrich streaming data in real-time on immutable streams of data before inserting it into ClickHouse. This reduces the load on ClickHouse for complex transformations and allows it to focus on serving queries efficiently.
Reduced Latency
By pre-processing data with Timeplus, the information inserted into ClickHouse is already optimized for querying, resulting in lower query latency.
Query Based Stream Processing
Updates to materialized views off of input streams happen independently without impacting each other due to the query based incremental processing.
Efficient Storage Utilization
Timeplus can perform initial aggregations and filtering, reducing the volume of data that needs to be stored in ClickHouse, thus optimizing storage usage.
Flexible Data Models
Timeplus can handle dynamic schemas and complex event processing, allowing for more adaptable data models before data reaches ClickHouse.
Scalable Real-Time Analytics
The combination of Timeplus for stream processing and ClickHouse for data storage and querying creates a scalable architecture for real-time analytics.
Complex Data Correlations
Timeplus can perform complex joins and unions on streaming data, creating denormalized records that can be directly inserted into ClickHouse, bypassing the limitations of ClickHouse Materialized Views.
Efficient Handling of Frequent, Small Writes
Timeplus is designed to handle high-frequency, small-batch data writes efficiently, addressing one of the key performance limitations of ClickHouse Materialized Views.
Timeplus Materialized Views
While both ClickHouse and Timeplus provide materialized views for handling large volumes of data, they have distinct approaches and capabilities.
HOW DOES IT WORK?
Timeplus materialized views leverage the full power of streaming SQL reading from any number of sources versus just acting on the block of data inserted into a source ClickHouse table. The streaming SQL continuously runs in the background and persists query results to the internal storage of the materialized view. The materialized views can be queried as a table via any SQL query or act as another source of data for another materialized view. The possibilities are limitless.
Alternatively you can set a target stream for the materialized view. It can be an append-only stream in Timeplus, or a Mutable Stream for UPSERT and fast OLAP queries, or an External Stream to write data to Apache Kafka, Apache Pulsar, or an External Table to write data to ClickHouse. This way, materialized views act as derivatives of upstream sources and can feed downstream sources too.
FEATURE HIGHLIGHTS:
Automatically updates results in the materialized view when the streaming SQL emits a new result, instead of when the source data changes. This can be tuned to emit upon certain completion criteria, like session window timeouts.
Supports joins of multiple streams, instead of a single table in ClickHouse Materialized Views, including arbitrary aggregations without using SummingMergeTree or being limited by functions in ClickHouse that support AggregationFunction.
Supports building a materialized view on top of another materialized view by joining with other streams.
Supports UNION and other complex JOIN operations, or Complex Event Processing (CEP).
Supports time-based windowing such as tumbling windows, hopping, and session windows.
Supports failover, checkpoint, and retry policies if an external downstream is temporarily unavailable.
Supports using a default internal stream to materialize the result, or set a target stream or external stream/table. This can be used for setting up streaming ETL pipelines and avoid the need for polling based Reverse ETL from ClickHouse.
Supports ad-hoc queries on the materialized views to serve most recent data by operational applications and BI Tools.
Supports pause and resume.
Each materialized view is maintained independently of others in terms of execution and thus does not impact the input sources or other materialized views.
Complementary Strengths:
Timeplus and ClickHouse
While ClickHouse Materialized Views offer powerful querying capabilities for pre-aggregated data, Timeplus excels in real-time stream processing. By combining these technologies, organizations can create a robust data pipeline that leverages the strengths of both systems and mitigates the limitations of ClickHouse Materialized Views.
Use Case: Real-Time Customer Analytics
Consider a scenario where an e-commerce platform wants to provide real-time customer analytics. Here's how Timeplus and ClickHouse can work together:
1.
Timeplus ingests real-time customer interaction data (clicks, purchases, reviews) from multiple sources.
2.
Timeplus performs stream-to-stream joins to enrich the data with customer profile information and product details or time based behavioral data
3.
Timeplus applies time-based windowing to calculate metrics like customer engagement scores and product popularity.
4.
The processed, enriched, and denormalized data is then inserted into ClickHouse.
5.
ClickHouse stores the pre-processed data, ready for efficient querying.
6.
Business analysts can now query ClickHouse for up-to-date customer analytics with low latency, without the need for complex joins or unions at query time.
Conclusion
While ClickHouse Materialized Views offer powerful capabilities for data aggregation and fast querying, they have significant limitations in handling real-time streaming data, especially with complex data correlations and frequent small-batch writes. Timeplus complements ClickHouse by providing robust stream processing capabilities, allowing organizations to create a comprehensive real-time data pipeline that addresses these limitations.
By using Timeplus to handle the complexities of real-time data processing, including complex joins and unions, and leveraging ClickHouse for efficient data storage and querying, organizations can achieve both low-latency data processing and high-performance analytics. This combination enables businesses to make data-driven decisions based on the most up-to-date and comprehensively processed information available, overcoming the constraints of using ClickHouse Materialized Views alone.
Disclaimer: This comparison involves products not owned by Timeplus. The information provided is based on public sources and personal research. We do not endorse or guarantee the accuracy of the product details. Please do your own research before making any decisions.
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