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GraphQL VS Spark Streaming

Compare GraphQL VS Spark Streaming and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

GraphQL logo GraphQL

GraphQL is a data query language and runtime to request and deliver data to mobile and web apps.

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
  • GraphQL Landing page
    Landing page //
    2023-08-01
  • Spark Streaming Landing page
    Landing page //
    2022-01-10

GraphQL features and specs

  • Efficient Data Retrieval
    GraphQL allows clients to request only the data they need, reducing the amount of data transferred over the network and improving performance.
  • Strongly Typed Schema
    GraphQL uses a strongly typed schema to define the capabilities of an API, providing clear and explicit API contracts and enabling better tooling support.
  • Single Endpoint
    GraphQL operates through a single endpoint, unlike REST APIs which require multiple endpoints. This simplifies the server architecture and makes it easier to manage.
  • Introspection
    GraphQL allows clients to query the schema for details about the available types and operations, which facilitates the development of powerful developer tools and IDE integrations.
  • Declarative Data Fetching
    Clients can specify the shape of the response data declaratively, which enhances flexibility and ensures that the client and server logic are decoupled.
  • Versionless
    Because clients specify exactly what data they need, there is no need to create different versions of an API when making changes. This helps in maintaining backward compatibility.
  • Increased Responsiveness
    GraphQL can batch multiple requests into a single query, reducing the latency and improving the responsiveness of applications.

Possible disadvantages of GraphQL

  • Complexity
    The setup and maintenance of a GraphQL server can be complex. Developers need to define the schema precisely and handle resolvers, which can be more complicated than designing REST endpoints.
  • Over-fetching Risk
    Though designed to mitigate over-fetching, poorly designed GraphQL queries can lead to the server needing to fetch more data than necessary, causing performance issues.
  • Caching Challenges
    Caching in GraphQL is more challenging than in REST, since different queries can change the shape and size of the response data, making traditional caching mechanisms less effective.
  • Learning Curve
    GraphQL has a steeper learning curve compared to RESTful APIs because it introduces new concepts such as schemas, types, and resolvers which developers need to understand thoroughly.
  • Complex Rate Limiting
    Implementing rate limiting is more complex with GraphQL than with REST. Since a single query can potentially request a large amount of data, simple per-endpoint rate limiting strategies are not effective.
  • Security Risks
    GraphQL's flexibility can introduce security risks. For example, improperly managed schemas could expose sensitive information, and complex queries can lead to denial-of-service attacks.
  • Overhead on Small Applications
    For smaller applications with simpler use cases, the overhead introduced by setting up and maintaining a GraphQL server may not be justified compared to a straightforward REST API.

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.

GraphQL videos

REST vs. GraphQL: Critical Look

More videos:

  • Review - REST vs GraphQL - What's the best kind of API?
  • Review - What Is GraphQL?

Spark Streaming videos

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 GraphQL and Spark Streaming)
Developer Tools
100 100%
0% 0
Stream Processing
0 0%
100% 100
JavaScript Framework
100 100%
0% 0
Data Management
0 0%
100% 100

User comments

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Social recommendations and mentions

Based on our record, GraphQL seems to be a lot more popular than Spark Streaming. While we know about 247 links to GraphQL, 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.

GraphQL mentions (247)

  • From REST to GraphQL: My First Impressions and Setup Experience
    Recently, I started exploring GraphQL while working on my MERN stack project. I learnt this through some youtube videos and some Other sources. Https://graphql.org/. - Source: dev.to / 14 days ago
  • Top 10 Programming Trends and Languages to Watch in 2025
    Sonja Keerl, CTO of MACH Alliance, states, "Composable architectures enable enterprises to innovate faster by assembling best-in-class solutions." Developers must embrace technologies like GraphQL, gRPC, and OpenAPI to remain competitive. - Source: dev.to / 26 days ago
  • 🚀 REST API vs. GraphQL: Which One Should You Use in 2025?
    📌 Learn more about GraphQL: https://graphql.org/. - Source: dev.to / 3 months ago
  • Next.js vs Nest.js: What to Choose in 2025?
    Nest.js has been most widely adopted in developing back-end applications such as RESTful APIs, GraphQL services, and microservices. With its modular design, this framework is well and truly set for large project management; it allows for smooth and efficient performance through built-in features such as dependency injection and strong middleware support. - Source: dev.to / 4 months ago
  • The Power of GraphQL: A Beginner’s Guide to Modern Web Development
    Overview: Managing data efficiently is crucial for delivering smooth user experiences in today's fast-paced digital world. One technology that has revolutionized data handling in web development is GraphQL. This query language for APIs has transformed the way developers interact with data sources, offering flexibility, efficiency, and speed. - Source: dev.to / 4 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 2 months 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 / 10 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 GraphQL and Spark Streaming, you can also consider the following products

gRPC - Application and Data, Languages & Frameworks, Remote Procedure Call (RPC), and Service Discovery

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

React - A JavaScript library for building user interfaces

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

Next.js - A small framework for server-rendered universal JavaScript apps

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