Apache Hadoop 3 is going to incorporate a number of enhancements over the Hadoop
Another option to get more data into memory is to reduce the block size of the data stored in disk. When a row is requested by client, the block corresponding to where the row is stored on disk store file is read into memory cache before sending it back the requested data to the client.
So by decreasing the block size more relevant data can be stored in cache which can improve read performance. Reducing the block size is not good for all scenarios. But here is an example where reducing the block size will help.
If the use case is to read the latest data about tickers stored in a row and the data size is 5 K in size. Since the probability that users will be reading same popular tickers repeatedly is high, the relevant data which we need to store in memory are the popular ticker data.
In this scenario reducing the block size to 8 K will improve considerably the amount of relevant data stored in cache and will improve the query performance.
One drawback of reducing the blocksize is the increase in index and meta data stored in store files and cache which may be small price to pay for the performance gain.
Given that physical servers currently come with large memory say GBcan we allocate HBase heapsizes of say 96 GB so that we can store large amount of data in cache?
The answer is no. We will look at the reason and an option to leverage the available physical memory next. More notes on this category can be found here.
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