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ClickHouse VS Spark Streaming

Compare ClickHouse VS Spark Streaming and see what are their differences

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  3. » Reviews
  4. » Alternatives

ClickHouse logo ClickHouse

ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
  • ClickHouse Landing page
    Landing page //
    2019-06-18
  • Spark Streaming Landing page
    Landing page //
    2022-01-10

ClickHouse features and specs

  • High Performance
    ClickHouse is designed for fast processing of analytical queries, often performing significantly faster than traditional databases due to its columnar storage format and optimized query execution.
  • Scalability
    The system is built to handle extensive datasets by scaling horizontally through distributed cluster configurations, making it suitable for big data applications.
  • Real-time Data Ingestion
    ClickHouse supports real-time data ingestion and can immediately reflect changes in query results, which is valuable for use cases requiring instant data processing and analysis.
  • Cost Efficiency
    The open-source nature of ClickHouse makes it a cost-effective option, especially when compared to other commercial data warehouses.
  • SQL Compatibility
    ClickHouse features strong SQL support, which makes it easier for individuals with SQL expertise to transition and use the platform effectively.
  • Compression
    ClickHouse employs advanced compression algorithms that reduce storage requirements and improve query performance.

Possible disadvantages of ClickHouse

  • Complexity in Setup
    Setting up and managing ClickHouse, particularly in a distributed cluster environment, can be complex and require a higher level of expertise compared to some other database systems.
  • Limited Transaction Support
    ClickHouse is optimized for read-heavy operations and analytics but does not support full ACID transactions, which limits its use for certain transactional use cases.
  • Ecosystem and Tooling
    While the ecosystem is growing, ClickHouse still has fewer tools and third-party integrations compared to more mature databases, which can limit its utility in some environments.
  • Resource Intensive
    Running ClickHouse, especially for large datasets, can be resource-intensive, requiring significant memory and CPU resources.
  • Limited User Management
    The platform has relatively basic user management and security features, which may not meet the needs of enterprises with strict compliance and governance requirements.

Spark Streaming features and specs

  • Scalability
    Spark Streaming is highly scalable and can handle large volumes of data by distributing the workload across a cluster of machines. It leverages Apache Spark's capabilities to scale out easily and efficiently.
  • Integration
    It integrates seamlessly with other components of the Spark ecosystem, such as Spark SQL, MLlib, and GraphX, allowing for comprehensive data processing pipelines.
  • Fault Tolerance
    Spark Streaming provides fault tolerance by using Spark's micro-batching approach, which allows the system to recover data in case of a failure.
  • Ease of Use
    Spark Streaming provides high-level APIs in Java, Scala, and Python, making it relatively easy to develop and deploy streaming applications quickly.
  • Unified Platform
    It provides a unified platform for both batch and streaming data processing, allowing reuse of code and resources across different types of workloads.

Possible disadvantages of Spark Streaming

  • Latency
    Spark Streaming operates on a micro-batch processing model, which introduces latency compared to real-time processing. This may not be suitable for applications requiring immediate responses.
  • Complexity
    While it integrates well with other Spark components, building complex streaming applications can still be challenging and may require expertise in distributed systems and stream processing concepts.
  • Resource Management
    Efficiently managing cluster resources and tuning the system can be difficult, especially when dealing with variable workload and ensuring optimal performance.
  • Backpressure Handling
    Handling backpressure effectively can be a challenge in Spark Streaming, requiring careful management to prevent resource saturation or data loss.
  • Limited Windowing Support
    Compared to some stream processing frameworks, Spark Streaming has more limited options for complex windowing operations, which can restrict some advanced use cases.

ClickHouse videos

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Spark Streaming videos

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Spark Streaming Vs Kafka Streams || Which is The Best for Stream Processing?

More videos:

  • Tutorial - Spark Streaming Vs Structured Streaming Comparison | Big Data Hadoop Tutorial

Category Popularity

0-100% (relative to ClickHouse and Spark Streaming)

ClickHouse

Spark Streaming

Databases
100 100%
Databases
0% 0
Stream Processing
0 0%
Stream Processing
100% 100
Relational Databases
100 100%
Relational Databases
0% 0
Data Management
0 0%
Data Management
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare ClickHouse and Spark Streaming

ClickHouse Reviews

Rockset, ClickHouse, Apache Druid, or Apache Pinot? Which is the best database for customer-facing analytics?
‍ClickHouse is an open-source, column-oriented, distributed, and OLAP database that’s very easy to set up and maintain. “Because it’s columnar, it’s the best architectural approach for aggregations and for ‘sort by’ on more than one column. It also means that group by’s are very fast. It’s distributed, replication is asynchronous, and it’s OLAP—which means it’s meant for...
Source: embeddable.com
ClickHouse vs TimescaleDB
Recently, TimescaleDB published a blog comparing ClickHouse & TimescaleDB using timescale/tsbs, a timeseries benchmarking framework. I have some experience with PostgreSQL and ClickHouse but never got the chance to play with TimescaleDB. Some of the claims about TimescaleDB made in their post are very bold, that made me even more curious. I thought it’d be a great...
Source: pradeepchhetri.xyz
20+ MongoDB Alternatives You Should Know About
ClickHouse may be a great contender for moving analytical workloads from MongoDB. Much faster, and with JSON support and Nested Data Structures, it can be great choice for storing and analyzing document data.
Source: www.percona.com

Spark Streaming Reviews

We have no reviews of Spark Streaming yet.
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Social recommendations and mentions

Based on our record, ClickHouse seems to be a lot more popular than Spark Streaming. While we know about 56 links to ClickHouse, we've tracked only 5 mentions of Spark Streaming. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

ClickHouse mentions (56)

  • How to Build a Streaming Deduplication Pipeline with Kafka, GlassFlow, and ClickHouse
    ClickHouse: A fast columnar database. It will be our final destination for clean data. And, for simplicity in this tutorial, we'll cleverly use it as our "memory" or state store to remember which events we've already seen recently. - Source: dev.to / 9 days ago
  • Why You Shouldn’t Invest In Vector Databases?
    In fact, even in the absence of these commercial databases, users can effortlessly install PostgreSQL and leverage its built-in pgvector functionality for vector search. PostgreSQL stands as the benchmark in the realm of open-source databases, offering comprehensive support across various domains of database management. It excels in transaction processing (e.g., CockroachDB), online analytics (e.g., DuckDB),... - Source: dev.to / 28 days ago
  • Twitter's 600-Tweet Daily Limit Crisis: Soaring GCP Costs and the Open Source Fix Elon Musk Ignored
    ClickHouse: ClickHouse is an open-source columnar database management system designed for high-performance analytics. It excels at processing large volumes of data and offers real-time querying capabilities. It’s probably the world’s fastest real-time data analytics system: ClickHouse Benchmark. - Source: dev.to / about 1 month ago
  • DeepSeek's Data Breach: A Wake-Up Call for AI Data Security
    Further investigation revealed that these ports provided direct access to a publicly exposed ClickHouse database—entirely unprotected and requiring no authentication. This discovery raised immediate security concerns, as ClickHouse is an open-source, columnar database management system designed for high-speed analytical queries on massive datasets. Originally developed by Yandex, ClickHouse is widely used for... - Source: dev.to / 4 months ago
  • Should You Ditch Spark for DuckDB or Polars?
    Clickhouse also has managed service (https://clickhouse.com/). - Source: Hacker News / 5 months ago
View more

Spark Streaming mentions (5)

  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / about 1 month ago
  • Streaming Data Alchemy: Apache Kafka Streams Meet Spring Boot
    Apache Spark Streaming: Offers micro-batch processing, suitable for high-throughput scenarios that can tolerate slightly higher latency. https://spark.apache.org/streaming/. - Source: dev.to / 9 months ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / over 1 year ago
  • Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
    Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / over 2 years ago
  • Spark for beginners - and you
    Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / over 3 years ago

What are some alternatives?

When comparing ClickHouse and Spark Streaming, you can also consider the following products

MySQL - The world's most popular open source database

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

Apache Doris - Apache Doris is an open-source real-time data warehouse for big data analytics.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

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