Review for BlinkDB (Eurosys'13)

1) Is the problem real?
The problem of real-time data processing is a big challenge. Previous works mainly focus on using sampling technique to meet with the real-time bound. There are mainly two types of these approximate data analysis techniques. The first kind is under strong assumptions about the query workload. It can perform well if the system knows the workload and query information. The second kind is to have fewer assumptions but with the performance varied a lot.

Given this, the user must make the trade-off between accuracy and flexibility.

The BlinkDB want to achieve high accuracy approximation without good knowledge about the workload/query. The problem is real.

2) What is the solution’s main idea (nugget)?

The main idea contains two parts: sample creation and sample selection.
For the sample creation module, based on historical frequencies and past QCS, it mainly choose a set of stratified samples with total storage costs below some user configurable storage threshold.

sample selection is built to select the sample for processing the query.  It uses an Error-Latency

Profile heuristic to efficiently choose the sample that will best satisfy the user-speciffied error or time bounds.

3) Why is solution different from previous work?

The previous work either has strong assumptions about the query workload, or high flexibility but the accuracy varies a lot.

The BlinkDB is try to achieve a better balance between the efficiency and generality for  analytics workloads.

4) Does the paper (or do you) identify any fundamental/hard trade-offs?

I think the most important thing is that the data sampling is offline. It cannot perform well a data sampling when the data changes fast.

5) Do you think the paper will be influential in 10 years? Why or why not?
Maybe, I think approximate data analysis is always a way to analysis data with hard time constrain. BlinkDB provides a good sampling model to achieve high accuracy of data analysis.