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EP54: Cache Systems Every Developer Should Know
This week’s system design refresher:
Cache Systems Every Developer Should Know (Youtube video)
TCP vs. UDP
Batch v.s. Stream Processing
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Cache Systems Every Developer Should Know
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?
TCP vs. UDP: 7 Differences You Should Know
Connection-oriented vs. connectionless
Three-way handshake vs. No handshake
Header (20 bytes) vs. (8 bytes)
Point-to-point vs. Unicast & Multicast & Broadcast
Congestion control vs. no congestion control
Reliable vs. lossy
Flow control vs. no flow control
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:
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.
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.
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.
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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