EP54: Cache Systems Every Developer Should Know

This week’s system design refresher:

• Cache Systems Every Developer Should Know (Youtube video)

• Serverless DB

• TCP vs. UDP

• Batch v.s. Stream Processing

• Job openings

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Cache Systems Every Developer Should Know

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Top 10 Architecture Characteristics / Non-Functional Requirements with Cheatsheet

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What is Serverless DB

Are serverless databases the future? How do serverless databases differ from traditional cloud databases?

Amazon Aurora Serverless, depicted in the diagram below, is a configuration that is auto-scaling and available on-demand for Amazon Aurora.

• Aurora Serverless has the ability to scale capacity automatically up or down as per business requirements. For example, an eCommerce website preparing for a major promotion can scale the load to multiple databases within a few milliseconds. In comparison to regular cloud databases, which necessitate the provision and administration of database instances, Aurora Serverless can automatically start up and shut down.

• By decoupling the compute layer from the data storage layer, Aurora Serverless is able to charge fees in a more precise manner. Additionally, Aurora Serverless can be a combination of provisioned and serverless instances, enabling existing provisioned databases to become a part of the serverless pool.

Over to you: Have you used a serverless DB? Does it save cost?

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TCP vs. UDP: 7 Differences You Should Know

1. Connection-oriented vs. connectionless

   

2. Three-way handshake vs. No handshake

   

3. Header (20 bytes) vs. (8 bytes)

   

4. Point-to-point vs. Unicast & Multicast & Broadcast

   

5. Congestion control vs. no congestion control

   

6. Reliable vs. lossy

   

7. Flow control vs. no flow control 

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Batch v.s. Stream Processing

• Batch Processing: We aggregate user click activities at end of the day.

• Stream Processing: We detect potential frauds with the user click streams in real-time.

Both processing models are used in big data processing. The major differences are:

1. Input
   Batch processing works on time-bounded data, which means there is an end to the input data.
   Stream processing works on data streams, which doesn’t have a boundary.

2. Timelineness
   Batch processing is used in scenarios where the data doesn’t need to be processed in real-time.
   Stream processing can produce processing results as the data is generated.

3. Output
   Batch processing usually generates one-off results, for example, reports.
   Stream processing’s outputs can pipe into fraud decision-making engines, monitoring tools, analytics tools, or index/cache updaters.

4. Fault tolerance
   Batch processing tolerates faults better as the batch can be replayed on a fixed set of input data.
   Stream processing is more challenging as the input data keeps flowing in. There are some approaches to solve this:
   
   a) Microbatching which splits the data stream into smaller blocks (used in Spark);
   b) Checkpoint which generates a mark every few seconds to roll back to (used in Flink).

Over to you: Have you worked on stream processing systems?

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