wavelink avalanche

Cause 1: Generally due to the effectiveness of a certain node, resulting in the other node hit rate drop, buffer missing data and go to the database to find,Causes the database server to collapse in a short time.Reason 2: Buffering cyclical effects, such as 6 hours, there is a buffer peak every 6 hours, serious even can cause the db to crash.Can I restart it? Restart DB in the short term and be crushed, but the buffer data more, repeatedly restarted repeatedly, the buffer was rebuilt, the server

Defined: Cache avalanche phenomenon is due to a cache node failure (or cache failure), causing other nodes cache hit rate drop, cache missing data to database query, a short time caused the database server crashes, restart after a short period of time and crash, but at this time the cache more, after a period, the database gradually stabilized. Workaround: Analyze user behavior and try to distribute the failure time points evenly. Control the number

Cache Avalanche Phenomenon The cache hit ratio of the other nodes is degraded due to the failure of one node. The actual data in the cache is to be found in the database. Causes the database to crash in a short time. May restart DB (database), short-term will be crushed, only a small number of cache. Once the DB restarts repeatedly, the cache is rebuilt. Cache is periodically invalidated. Expires every 6 hours. Then a request spike occurs every 6 ho

What is avalanche? I saw an avalanche as I read the article. Baidu once circled and google again. it seems that no exact match was found for the definition of this term in the code. Please give me some advice! ------ Solution ---------------------- provide the context ------ solution ---------------------- what is the solution? ------ Solution ---------------------- what is snow

As we all know (a bit of research on cryptography), des is encrypted every 8-bit plaintext .. Des has a good avalanche effect in every 8-bit plaintext encryption (that is, changing the plaintext or key to 1 bit will cause a huge difference in the ciphertext ). However, this also determines that the avalanche effect of des on the simultaneous encryption of a large number of plain text is not obvious. Here is

Preface To design a caching system, the problem that has to be considered is: cache penetration, cache breakdown and failure of avalanche effect.Cache Penetration Cache penetration means querying a data that does not exist, because the cache is written passively when it is not hit, and for fault tolerance, if no data is found from the storage layer, the cache is not written, which causes the nonexistent data to be queried at the storage level for eac

Directory Cache breakdown/Penetration/avalanche Intro Cache breakdown Cache penetration Cache avalanche Reference Contact Cache breakdown/Penetration/Avalanche intro Using caching requires understanding several cache issues, cache breakdown, cache penetration, and cache avalanches, and you need to understand the cau

GitHub has a similar solution for open source, which readers can refer to:Https://github.com/erikdubbelboer/Redis-Lua-scaling-bloom-filterUsing a filter to solve penetration problemsThis approach is suitable for applications where data hits are low, data is relatively fixed in real-time (usually a large dataset), and code maintenance is more complex, but the cache space is less expensive. comparison of two alternatives Here are two solutions to the problem of cache penetration (in fact the prob

The testing tool uses the Avalanche 2200 provided by Spirent Communications. Avalanche 2200 simulates the actual user sending requests to the server, including SMTP and POP3), and provides detailed test results based on the response. It has the following features: it can simulate hundreds of thousands of clients to send requests to the server; can simulate real network applications; can generate 20000 conne

the natural cache. The request is sent directly to the database. If the concurrent access to this key is too large, the database will be overwhelmed. Solution: 1. Use a filter to hash all data that cannot exist in the database to a large bitmap. If the key does not exist in the database, it will be intercepted by bitmap. 2. Check the key that is null and the resume key value pair in the cache, which is a little shorter than the expiration time, such as 5 minetes.Cache

Cache avalancheThe cache avalanche may be because the data is not loaded into the cache, or the cache is invalidated by a large area at the same time, causing all requests to go to the database, causing the database CPU and memory load to be too high, or even downtime.Solution Ideas:1, using the lock count, or use a reasonable number of queues to avoid cache failure when the database caused too much pressure. This approach can alleviate the pressure o