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From "Sihua Zhou (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (FLINK-8601) Introduce ElasticBloomFilter for Approximate calculation and other situations of performance optimization
Date Sat, 21 Apr 2018 04:15:00 GMT

     [ https://issues.apache.org/jira/browse/FLINK-8601?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Sihua Zhou updated FLINK-8601:
------------------------------
    Summary: Introduce ElasticBloomFilter for Approximate calculation and other situations
of performance optimization  (was: Introduce PartitionedBloomFilter for Approximate calculation
and other situations of performance optimization)

> Introduce ElasticBloomFilter for Approximate calculation and other situations of performance
optimization
> ---------------------------------------------------------------------------------------------------------
>
>                 Key: FLINK-8601
>                 URL: https://issues.apache.org/jira/browse/FLINK-8601
>             Project: Flink
>          Issue Type: New Feature
>          Components: DataStream API, State Backends, Checkpointing
>    Affects Versions: 1.5.0
>            Reporter: Sihua Zhou
>            Assignee: Sihua Zhou
>            Priority: Major
>
> h3. Backgroud
> Bloom filter is useful in many situation, for example:
>  * 1. Approximate calculation: deduplication (eg: UV calculation)
>  * 2. Performance optimization: eg, [runtime filter join|https://www.cloudera.com/documentation/enterprise/5-9-x/topics/impala_runtime_filtering.html]
>        By using BF, we can greatly reduce the number of queries for state data in a stream
join, and these filtered queries will eventually fail to find any results, which is a poor
performance for rocksdb-based state due to traversing ```sst``` on the disk. 
> However, based on the current status provided by flink, it is hard to use the bloom filter
for the following reasons:
>  * 1. Serialization problem: Bloom filter status can be large (for example: 100M), if
implement it based on the RocksDB state, the state data will need to be serialized each time
it is queried and updated, and the performance will be very poor.
>  * 2. Data skewed: Data in different key group can be skewed, and the information of
data skewed can not be accurately predicted before the program is running. Therefore, it is
impossible to determine how much resources bloom filter should allocate. One way to do this
is to allocate space needed for the most skewed case, but this can lead to very serious waste
of resources.
> h3. Requirement
> Therefore, I introduce the PartitionedBloomFilter for flink, which at least need to meet
the following features:
>  * 1. Support for changing Parallelism
>  * 2. Only serialize when necessary: when performing checkpoint
>  * 3. Can deal with data skew problem: users only need to specify a PartitionedBloomFilter
with the desired input, fpp, system will allocate resource dynamic.
>  * 4. Do not conflict with other state: user can use KeyedState and OperateState when
using this bloom filter.
>  * 5. Support relax ttl (ie: the data survival time at least greater than the specified
time)
> Design doc:  [design doc|https://docs.google.com/document/d/1s8w2dkNFDM9Fb2zoHwHY0hJRrqatAFta42T97nDXmqc/edit?usp=sharing]



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