wavelink avalanche

, effective Java recommends using single element enumerations as much as possible to implement a single example. 8 Some personal thinking How do enumerated cases prevent reflection attacks?We have to consider the implementation of the enumeration.The above enumerated class is compiled to be the following format: Public abstract class Singleton extends Enum Class is abstract, so it cannot be instantiated, and reflection is powerless. 9 Conclusion More and more feel that their grasp of the founda

in the same Zone; Periodically update and filter the list of service instances from Eureka; Select the address of an instance in the list of service registrations from the Server, based on user-specified policies; Service calls are made through restclient. Service call end fusing--hystrix avalanche effectIn a microservices architecture, there are often multiple service layer calls, and the failure of the underlying service can lead t

Fuse Avalanche effectIn a microservices architecture, there are often multiple service layer calls, and the failure of the underlying service can lead to cascading failures that could result in an entire system being unavailable, a phenomenon known as service avalanche effect. The service avalanche effect is a process in which the service consumer is not availabl

Note: The concept part of the previous article excerpt from the Blog Garden Pure SmileFuse Avalanche effectIn a microservices architecture, there are often multiple service layer calls, and the failure of the underlying service can lead to cascading failures that could result in an entire system being unavailable, a phenomenon known as service avalanche effect. The service

be 1/4, each instance of the memory of the most 1/2; if 24 core 120G server, it is recommended to deploy 18 Reids instances, 5G per instance of memory, the actual use of not more than 3G of data ~ Reids is the cache inherited the advantages and disadvantages of caching, high performance is the advantage, disadvantage: cache penetration, Cache avalanche. 1. Cache penetration: Cache penetration refers to querying a certain non-existent data, because th

caching large amounts of data.Cache avalanche: when a large number of caches expire at the same time or the process is restarted, a large amount of cache penetration may occur, and too many requests may hit the relational database, which may cause the relational database to crash, this causes more unavailability issues.Why is redis?Redis and other databases can solve two problems in-process cache: Independent deployment without affecting other servic

corresponding value value to the corresponding remainder of the memcache server, generally this hash function CRC32 (key) % 3CRC32 () This function can make a string into a 32-bit integer?Cons: When a server goes down or needs to add a server, this time the cache data basically all fail, because the divisor has changed. Not strict formula, hit rate = Fetch to Data/Total 1/n N represents the number of servers?The problem is raised: when the memcache down, the cache data fails, and this time the

characteristic curve of TVs TVS have the same circuit symbols as regular zener diodes. Its forward characteristic is the same as the ordinary diode, and the reverse characteristic is typical PN junction avalanche device. Under the action of the transient peak current pulse, the current flowing through the TVs, from the original reverse leakage current ID up to IR, its bipolar voltage from the rated reverse shutdown voltage VWM rise to the breakdown v

database caused by so many requests. The specific implementation depends on the business scenario, but the solution is to avoid as many requests as possible in an instant, and avoid putting too much pressure on the database when a large number of requests are generated. The method of Throttling depends on the business scenario. Not all scenarios are suitable for throttling. Therefore, the maximum number of connections from the backend database to the database is limited. As long as the reques

instantaneous concurrency generally will not cause any practical problems. So how to solve this problem from the code level depends on how you design the system. Second-level cache. Put the data in a key that has a longer expiration time. When the avalanche lock, ensure that only one PHP process access to the database, the rest of the see Lock is no longer accessible, directly return the data in the cache. The effect is that although several people

/Total 1/n N represents the number of serversThe problem is raised: When the memcache down, the cache data fails, and this time the MySQL pressure will surge,At this time, MySQL will be down, and then restart Mysql,mysql will be down again in a short period of time, then, a little delay (the cache has been re-established a part), and downtime. As time went on, MySQL basically stabilized and the cache system was built successfully. Because the cache data does not exist, all requests are turned to

Springcloud fuses the specific use of Hystrix, springcloudhystrix Speaking of springcloud's overall lockdown reminds me of the lockdown in the stock market last year, and many painful understandings. The impact of the random lockdown on the entire system is catastrophic. Well, let's talk about the future. Fuses Avalanche effect In a microservice architecture, multiple service layers are usually called. Faults of Basic Services may lead to cascade fail

of SM3 cipher hashing algorithm SM3 the design of cryptographic hashing algorithm mainly follows the following principles: Can effectively resist the bit tracking method and other analytical methods; Under the precondition of guaranteeing safety, the comprehensive performance index is equivalent to the SHA-256 under the same conditions. (1) Design principle of compression function The design of compression function has the characteristics of clear structur

automatically has the function of registry, load Balancing, failover. hystrix There are often multiple service layer calls in the MicroServices architecture, and the failure of the underlying service can lead to cascading failures, which in turn will result in the entire system being unavailable. This phenomenon is called the service avalanche effect. The service avalanche effect is a process in whi

generated purely random, then there is an equal chance that any value between 128 and 255 will occur, but at the same time there is a 0% chance that a value less than 128 will occur. All prngs, whether it is a hash function, a password, a msequences, or any other generator that generates a bit stream, will have such a bit bias. Most prngs they will try to converge to a certain value, the stream cipher is an example, and the other generator is better at an indeterminate bit bias. Mixing or bit-s

query string in the URL, and the "If-none-match" message in the HTTP header. Furthermore, this cache control "command" is bound to pass through layers and eventually pass to the heterogeneous "table" module of the underlying storage. Caching systems often have two problems to confront and consider: The avalanche effect of cache penetration and failure. Cache penetration refers to querying a certain non-existent data, because the cache is not hit when

To design a caching system, the problem that has to be considered is the avalanche effect of cache penetration, cache breakdown and failure.Cache breakdownCache penetration refers to querying a certain non-existent data, because the cache is not hit when the passive write, and for fault-tolerant consideration, if the data from the storage layer is not written to the cache, which will cause this non-existent data each request to the storage layer to qu

set, called the whole domain (universe), and the complete set of hashes (0,1,...,m-1}) is represented by S. Think of keywords as 0 or 1 strings, or binary representations. Hash (hash) English original meaning is "mixed", "patchwork", "re-expression". In fact, a hash is an action that reverses the sort, in which the elements in the collection are sorted in some way, such as a dictionary order, and hashed by calculating the hash value, breaking the original relationship between the elements so th

actual operation should be noted.4 . Service Protection Service protection in principle is to avoid similar avalanche effect, try to control the anomaly around the service, do not spread. When it comes to the avalanche effect, it is also necessary to mention the Dubbo own retry mechanism, the default 3 times, when the failure will be retried, so that at some point in time performance problems, and then the

is 11.7%. Similarly, when the pre- ing space is large, the algorithm must have sufficient strength to ensure that it cannot easily find people with the same birthday. The third is the uniformity of ing distribution and the uniformity of differential distribution. In the hash result, BITs 0 and 1 are equal, and the total number of BITs should be roughly equal, more than half of the bit changes in the hash result are called avalanche effect, at least