Kafka vs SQS – What’s the Difference ? (Pros and Cons)
Kafka vs SQS – What’s the Difference ? (Pros and Cons). Streaming data involves event handling, through processing, storage and transformation.
There are two platforms Kafka vs SQS with important features and are mainly programmed to queue messages. But is it only? If you’re considering whether Kafka or AWS SQS is best for your use case, then continue reading. We will learn about how they handle messaging differently, features and pros and cons. We’ll talk best uses cases for both tools and create a comparison.
Amazon Simple Queue Service SQS is a managed message queuing service that technical professionals and developers use to send, store and retrieve multiple messages of various sizes all at the same time.
So let’s start with Kafka vs SQS – What’s the Difference ? (Pros and Cons).
What is a Kafka?
Firstly Apache Kafka is an open source distributed messaging platform. Designed to manage streaming data in real time for distributed streaming, pipetting, and playback of data streams for fast and scalable operations.
Secondly Apache Kafka is, of course, an optimized data warehouse in terms of data transfer and processing. The transferred data is continuously generated by thousands of data sources, typically by sending data records. Streaming platforms have to deal with the continuous flow of data, sequentially and incrementally.
Thirdly Kafka is a middleware based solution that works by maintaining data streams as logs on a series of servers. Moreover Kafka servers span multiple data centers and provide data robustness by storing messages in threads across multiple server instances.
Use Cases for Apache Kafka
Specifically Kafka is one of the fastest growing open source communications solutions. Design pattern gives you an excellent logging mechanism for distributed systems.
Interestingly Kafka is designed to stream logs in real time and is ideal for the needs of:
- Reliable data exchange between different components.
- Has built in support for data/message playback.
- Splits messaging workloads based on application needs.
- Real time transmission of data processing.
Features of using Apache Kafka
- Handles scalability in all four dimensions: event producers, event handlers, event consumers and event connectors. In other words, Kafka expands easily without stopping.
- Very productive for both publishing and subscribing messages. Maintains stable performance despite storing many terabytes of messages.
- Distributed, partitioned, replicated and fault tolerant, leads to amazing reliability.
- Forks new data streams using product data streams.
- Especially Kafka Mirror maker provides transcription support for your group.
- Replication messages are replicated across multiple data centers or cloud regions.
- You can use these passive/passive schemas for backup and recovery, or to bring data closer to users or to support data area requirements.
- Durable solution as it uses distributed commit log, that means messages persists on disk as fast as possible.
- Clusters of Kafka handle large failures and databases.
Pros and cons of using Apache Kafka
Pros
- Very accessible.
- Reduces the need for multiple integrations.
- Low latency.
- Batch approach.
- Real time handling.
- High throughput.
- Distributed system.
- Operational Metrics/KPIs.
- Log aggregation.
Cons
- Lacks some message paradigms.
- Reduces performance.
- Message tweaking issues.
- Not a complete set of monitoring tools.
- Do not support wildcard topic selection.
- Streaming ETL.
What is a SQS?
Here the SQS stands for Simple Queue Service, is a service operated by AWS for managing message queues.
Subsequently one service sends messages to the queue and another service receives them. However it can be used for many purposes.
Importantly SQS frees developers from the hassle of configuring and managing queue structures as a managed service. Especially this service provides you with everything you need out of the box and is easily scalable to meet your demand.
Significantly ideal solution for time critical projects and small to medium development teams.
How SQS works?
The most important high level actions are:
- Send Message: Services send requests to queues that are used by other services.
- Receive Message: Another service requests to receive queued messages.
- Delete Message: Once the message is successfully processed, the user removes it from the queue, should he wishes to do so.
Nevertheless SQS does not work as a database, so users cannot decide which messages in the queue to receive. However, you can limit the number of messages you will receive at any one time.
When a message is received, SQS temporarily removes it from the queues so it is never sent twice. Moreover the message is hidden for a while and the user processes it and removse it from the list.
If the message is not removed after the visibility timeout, it is then returned to the queue and receives a future requests. Timeout interval for visibility can be set.
Features of using SQS
- You are paying for the number of orders in the queue.
- Excellent service that improves the efficiency, reliability and performance of applications.
- Messages in the queue are delivered at least once. So guaranteed message delivery without message loss.
- And those messages that cannot be processed are saved in the garbage queue.
- There are two types of queues: standard queues and FIFO queues. In the default queue, messages are retrieved randomly.
- Multiple components run in a single queue. Besides SQS uses a blocking mechanism, if one component consumes a message, it hides it from other components.
- After successful processing, the message is removed from the queue. If the message cannot be processed, it remains in the queue and is visible to all components. A feature called timeout visibility.
- No data loss.
- Each request is handled independently.
Pros and cons of using SQS
Pros
- Automatic deduplication for FIFO queues.
- A separate queue for unprocessed messages.
- Options for Standard and FIFO queues.
- Scalability.
- Pay for what you use.
- Ease of setup and to use..
- Low cost.
Cons
- Lack of support for broadcast messages.
- High cost at scale.
- Reduced control over performance.
Now it is time with Kafka vs SQS – What’s the Difference ? to learn about their differences.
Kafka vs SQS - Key Differences
On one side hand, Kafka is described in detail as a “fault tolerant and high performance publish subscribe messaging system.” So Kafka is a distributed, segmented and redundant commit log service. Provides the functionality of the email system but with a unique design.
The developers describe Amazon SQS as a “fully managed message queue service”. Hence you can transfer any amount of data without dropping messages or requiring other services to be always available. With SQS, you reduce the administrative burden of running and scaling a highly available message pool. While getting what you use at an affordable price.
In nutshell they are both messaging systems, but they are completely different:
Use cases
Kafka
- Overall a general purpose message broker.
- Stream processing framework.
- Highly scalable system for large workloads that need to send messages in batches (for smooth message passing).
- Topics in Kafka consists of multiple sections that are read in parallel by different consumers of a group of consumers, which gives us a very good performance.
- For example, if you need to create a very busy broadcast system, Kafka is a good fit.
- Ideal for streaming applications where throughput is a major concern
SQS
- Operated by Amazon (so you don’t need to support the infrastructure).
- Better suited for events, when you need to intercept a message (event) from the client, the message is automatically pulled from the queue.
- Not as fast as Kafka and not suitable for large workloads.
- Better for events with few events per second.
- Great fit if you want to respond to the download of an S3 file (start processing that file).
Message Model
Message model of Apache Kafka follows publish subscriber model, whereas SQS is pull based streaming. Also SQS has two types of queues: FIFO and Standard and they are focused on the successful delivery and processing of messages by individual clients.
Message size
Maximum Message size for Kafka is 1MB and configurable. But for SQS Max Message size 256KB.
Deduplication
Deduplication is supported in Kafka with dedupe “worker” is a Go program which reads off the Kafka input partitions. Contrarily SQS does not support deduplication if the same data is generated multiple times. Also it attempts to delete messages based on the deduplication ID and deduplication interval.
Ordering at scale
Ordering at scale is supported with Kafka. Produced messages are always consumed in order irrespective of the number of items in the queue.
With SQS FIFO queue looks through the first 20k messages to determine available message groups.
Limit on the Number of groups/topic/partitions
Although in Kafka the limit is quite high, the number of topics/sections is usually in the thousands (may increase depending on group size).
But with SQS there is no set quota for the number of message groups in the first in, first out queue.
Partition management
Partition management with Kafka where the sections are created or added and managed by the user with Kafka.
On the other side SQS controls the number of partitions and they are increased or decreased based on load and usage patterns.
Thank you for reading Kafka vs SQS – What’s the Difference ? (Pros and Cons). We shall conclude.
Kafka vs SQS – What’s the Difference ? Conclusion
Summing up with SQS, you reduce the administrative tasks of running and scaling a highly available message pool, while getting what you use at an affordable price. On the other hand, Kafka is best for high loads of data and leverages sequential disk I/O operations so needs less hardware.
We say that Kafka is more scalable and should be used as a pipeline to process the stream. Instead, SQS is designed to move background tasks to an asynchronous pipeline.
Read more of our Kafka content here.
