/1 Data is cached everywhere, from the front end to the back end!
This diagram illustrates where we cache data in a typical architecture.
This diagram illustrates where we cache data in a typical architecture.
/2 There are ๐ฆ๐ฎ๐ฅ๐ญ๐ข๐ฉ๐ฅ๐ ๐ฅ๐๐ฒ๐๐ซ๐ฌ along the flow.
๐น 1. Client apps: HTTP responses can be cached by the browser. We request data over HTTP for the first time; we request data again, and the client app tries to retrieve the data from the browser cache first.
๐น 1. Client apps: HTTP responses can be cached by the browser. We request data over HTTP for the first time; we request data again, and the client app tries to retrieve the data from the browser cache first.
/3
๐น 2. CDN: CDN caches static web resources. The clients can retrieve data from a CDN node nearby.
๐น 3. Load Balancer: The load Balancer can cache resources as well.
๐น 2. CDN: CDN caches static web resources. The clients can retrieve data from a CDN node nearby.
๐น 3. Load Balancer: The load Balancer can cache resources as well.
/4
๐น 4. Messaging infra: Message brokers store messages on disk first, and then consumers retrieve them at their own pace. Depending on the retention policy, the data is cached in Kafka clusters for a period of time.
๐น 4. Messaging infra: Message brokers store messages on disk first, and then consumers retrieve them at their own pace. Depending on the retention policy, the data is cached in Kafka clusters for a period of time.
/5
๐น 5. Services: There are multiple layers of cache in a service. If the data is not cached in CPU cache, the service will try to retrieve the data from memory. Sometimes the service has a second-level cache to store data on disk.
๐น 5. Services: There are multiple layers of cache in a service. If the data is not cached in CPU cache, the service will try to retrieve the data from memory. Sometimes the service has a second-level cache to store data on disk.
/6
๐น 6. Distributed Cache: Distributed cache like Redis hold key-value pairs for multiple services in memory. It provides much better read/write performance than the database.
๐น 6. Distributed Cache: Distributed cache like Redis hold key-value pairs for multiple services in memory. It provides much better read/write performance than the database.
/7
๐น 7. Full-text Search: we sometimes need to use full-text searches like Elastic Search for document search or log search. A copy of data is indexed in the search engine as well.
๐น 7. Full-text Search: we sometimes need to use full-text searches like Elastic Search for document search or log search. A copy of data is indexed in the search engine as well.
/8
๐น 8. Database: Even in the database, we have different levels of caches:
- WAL(Write-ahead Log): data is written to WAL first before building the B tree index
- Bufferpool: A memory area allocated to cache query results
- Materialized View
๐น 8. Database: Even in the database, we have different levels of caches:
- WAL(Write-ahead Log): data is written to WAL first before building the B tree index
- Bufferpool: A memory area allocated to cache query results
- Materialized View
/9
- Transaction log: record all the transactions and database updates
- Replication Log: used to record the replication state in a database cluster
- Transaction log: record all the transactions and database updates
- Replication Log: used to record the replication state in a database cluster
/10 ๐ Over to you: With the data cached at so many levels, how can we guarantee the ๐ฌ๐๐ง๐ฌ๐ข๐ญ๐ข๐ฏ๐ ๐ฎ๐ฌ๐๐ซ ๐๐๐ญ๐ is completely erased from the systems?
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/11 I hope you've found this thread helpful.
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