Details
-
New Feature
-
Status: Closed
-
Major
-
Resolution: Won't Fix
-
0.99.0
-
None
-
None
Description
Cross-row transaction is a desired function for database. It is not easy to keep ACID characteristics of cross-row transactions in distribute databases such as HBase, because data of cross-transaction might locate in different machines. In the paper http://research.google.com/pubs/pub36726.html, google presents an algorithm(named percolator) to implement cross-row transactions on BigTable. After analyzing the algorithm, we found percolator might also be a choice to provide cross-row transaction on HBase. The reasons includes:
1. Percolator could keep the ACID of cross-row transaction as described in google's paper. Percolator depends on a Global Incremental Timestamp Service to define the order of transactions, this is important to keep ACID of transaction.
2. Percolator algorithm could be totally implemented in client-side. This means we do not need to change the logic of server side. Users could easily include percolator in their client and adopt percolator APIs only when they want cross-row transaction.
3. Percolator is a general algorithm which could be implemented based on databases providing single-row transaction. Therefore, it is feasible to implement percolator on HBase.
In last few months, we have implemented percolator on HBase, did correctness validation, performance test and finally successfully applied this algorithm in our production environment. Our works include:
1. percolator algorithm implementation on HBase. The current implementations includes:
a). a Transaction module to provides put/delete/get/scan interfaces to do cross-row/cross-table transaction.
b). a Global Incremental Timestamp Server to provide globally monotonically increasing timestamp for transaction.
c). a LockCleaner module to resolve conflict when concurrent transactions mutate the same column.
d). an internal module to implement prewrite/commit/get/scan logic of percolator.
Although percolator logic could be totally implemented in client-side, we use coprocessor framework of HBase in our implementation. This is because coprocessor could provide percolator-specific Rpc interfaces such as prewrite/commit to reduce Rpc rounds and improve efficiency. Another reason to use coprocessor is that we want to decouple percolator's code from HBase so that users will get clean HBase code if they don't need cross-row transactions. In future, we will also explore the concurrent running characteristic of coprocessor to do cross-row mutations more efficiently.
2. an AccountTransfer simulation program to validate the correctness of implementation. This program will distribute initial values in different tables, rows and columns in HBase. Each column represents an account. Then, configured client threads will be concurrently started to read out a number of account values from different tables and rows by percolator's get; after this, clients will randomly transfer values among these accounts while keeping the sum unchanged, which simulates concurrent cross-table/cross-row transactions. To check the correctness of transactions, a checker thread will periodically scan account values from all columns, make sure the current total value is the same as the initial total value. We run this validation program while developing, this help us correct errors of implementation.
3. performance evaluation under various test situations. We compared percolator's APIs with HBase's with different data size and client thread count for single-column transaction which represents the worst performance case for percolator. We get the performance comparison result as (below):
a) For read, the performance of percolator is 85% of HBase;
b) For write, the performance of percolator is 60% of HBase.
The drop derives from the overhead of percolator logic, the read performance is about 10% lower compared to that reported in percolator paper(94% for percolator). The write performance is much better compared to that reported in percolator paper(23% for percolator). We improve the performance of single-column transaction(also for single-row transaction) by only writing MemStore in prewrite-phase which will reduce one time HLog's write.
4. MapReduce Support. We implement a group of classes to support read data by themis transaction in Mapper job and write data by themis transaction in Reduce job.
5. The master branch of themis(https://github.com/XiaoMi/themis) is based on HBase 0.94, we also create a branch(https://github.com/XiaoMi/themis/tree/for_hbase_0.98) to support hbase 0.98.
We are glad to share current percolator implementation and hope this could provide a choice for users who want cross-row transactions because it does not need to change the code and logic of origin HBase. Comments and discussions are welcomed.