How to make personalized recommendations instantly accessible? Cloud native database gaussdb (for redis) to help

With the development of Internet Information Technology

Personalized recommendation has already been integrated into our life

All kinds of information collected in mobile phones

There is... Behind it TA

As a leading content ecosystem service platform in China , Shanghai Yueke Information Technology Co., Ltd （ abbreviation " Reading guests "） Drive operations through data analysis , Provide content ecosystem services on a large scale , And provide advertising technology services based on content scenes , Accurately match content and users with technology , Maximize content revenue .

Readers have strong content services and advertising capabilities , More than ten thousand content updates and hundreds of millions of exposures every day . Huge data volume and massive high concurrency , It challenges the database that supports the business application of reading customers .

Storage problems under the surge of data

Database as the cornerstone of carrying massive data , Bear the heavy responsibility of guarding enterprise data assets , It also plays a key role in the digital transformation of enterprises . Data volume surges , What readers use is based on ECS self-built Redis Databases are under great pressure in terms of high concurrency and stability , The cost also rises , The specific pain points are as follows ：

• Performance issues
  ： In the configuration cache scenario , The reader used Redis Store configuration policy information . There are usually some big key, Big key Open source Redis There are often performance problems in blocking requests , Therefore, the problem of slow query often occurs , There are also a lot of alarms in Yueke's own monitoring group every day .

• Massive data with high concurrent access
  ： Because the business adopts distributed deployment , Yes Redis There are a lot of concurrent requests , build by oneself sentinel sentry Redis The number of connections is maintained at 3w, Open source Redis Unbearable , This leads to frequent business access timeouts , You even need to restart self built Redis. Again , A lot of alarms are received every day .

• Data storage is expensive
  ： The amount of data is surging , Bron filters protobuf More and more serialized data , increased TB level . And open source Redis Memory cost pain points 、 Stability pain points begin to appear , Bring certain pressure to business operation .

• Relocation compatibility concerns
  ： The customer has built two different architectures from the beginning Redis colony , Namely Cluster Clusters and Sentinel colony . Each cluster corresponds to the corresponding client code , And do not support each other . If you choose to go to the cloud , Readers must modify their business code , Then reissue the version 、 go online , The burden of business transformation is large .

Personalized recommendations in the cloud native era

The age of cloud Nativity , Cloud native database based on unified cloud infrastructure , Become the first choice for enterprises to go to the cloud . Yueke follows the development trend of the times , Chose Huawei cloud native database GaussDB（for Redis） As the data base of enterprise digital transformation , It completely replaces the original self built Redis database , Business development has reached a new level .

Performance is remarkable , Content recommendation is faster

Configure the cache business for readers key Performance issues ,GaussDB（for Redis） use

Distributed architecture

and

Multithreading

The way of combination , Provides excellent performance , Ensure the continuous and efficient operation of the business . Compared to open source Redis The single thread architecture of , GaussDB（for Redis） The multi-threaded architecture of has more advantages , Even if there is a big key, It will not cause global performance damage . After successful relocation , Readers' own monitoring group responded to the timeout alarm, which was greatly reduced , Configure cache service response in a timely and efficient manner , Content recommendations reach the client faster .

Mass storage , Content recommendation is more stable

GaussDB(for Redis) Provide exclusive connection resources , Customers will build their own sentinels Redis Move to 4 node GaussDB(for Redis) After the instance , Business is really exclusive

4w

Connect several resources , And are within the appropriate threshold , Very stable operation , It completely solves the problem of connection number of customer reading business , Under the 100 million level flood peak scenario, you can also calmly face , Content recommendation is more stable .

Blum filter business cost savings 80%

GaussDB（for Redis） Adopt the architecture of separation of storage and calculation , You can buy and calculate independently 、 Storage resources , Avoid open source Redis The frequent waste of computing power costs ; It has strong data compression capability , Especially for the... In the bloom filtering scene protobuf Serializing data works wonders , Realized TB Level data to GB Effective compression of level , The release of the

80%

The cost of storage , Completely beyond the customer's imagination , It also paves the way for customers' future business growth .

There is no need to transform the application , One click relocation

GaussDB（for Redis）  Provide “Proxy Universal ” Instance type , Compatible with StandAlone client 、Cluster Client and Sentinel client , There is no need to modify the client business code , And it did “ One architecture is fully compatible ”、“ Business relocation 0 reform ”, Completely dispel the concerns of readers about relocation compatibility . Supported by the R & D team , All dozens of self built units were completed in one week Redis, It realizes efficient and smooth senseless migration .

Cloud native database GaussDB（for Redis） It not only improves the service efficiency of reading customers , Make personalized recommendation faster and more stable , It also reduces storage and transformation costs , It has laid a cloud foundation for the future development of the enterprise , Help readers achieve higher quality information touch .

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