We use Thrift c++ client library (0.7/0.8) to communicate with Apache Cassandra (1.0), and we need to frequently get intensive data from Cassandra. The type of data got has the following definition(multiget_slice):
std::map<std::string, std::vector<ColumnOrSuperColumn> >, where ColumnOrSuperColumn is a struct composed of several std::map with std::string keys.
Supose we have 1M data, and each time we got 1k, it means 1k records will exist in such struct as "std::map<std::string, std::vector<ColumnOrSuperColumn> >", then we need to call thrift RPC 1K times. While we destroy the above object of "std::map<std::string, std::vector<ColumnOrSuperColumn> >" immediately after the RPC, which means we do nothing but just perform the RPC operation. During that period, we found that the memory consumption keeps growing, evenif we attach jemalloc to the process for memory defragmentation.
No matter how we tune the batch size, say the above 1k, ranging from 10 to 20k, the memory fragmentation keeps a high percentage, it means given more data, say 10M, just such RPC operation will eat up the memory: In fact, our process was killed by OS due to too much memory consumption.
We believe that the current design of memory usage of Thrift cpp client has caused too much memory fragmentation and the issue appears to be more serious given more data as well as more complicated struct as defined in Cassandra.
I suggest to provide memory pool for Thrift cpp library.