I used below MultiIndexContainer typedef multi_index_container, const_mem_fun, const_mem_fun > >, ordered_unique< composite_key, const_mem_fun, const_mem_fun > > > > PositionSummaryContainer; And I inserted 10000 insts*36 accounts*100 days=36 million records //Begin testing of the multiIndexContainter std::cout << "Begin inserting data from array into the multiIndexContainter" << std::endl; timer.reset(); timer.begin(); for (int i = 0; i < numOfDays_; i++) { for (int j = 0; j < accountSize_; j++) { for (int k = 0; k < instSize_; k++) { PositionSummary* ps = psArray_[(i * accountSize_ + j) * instSize_ + k]; uniqueIndex.insert(ps); } } } printMemoryUsage(); timer.end(); std::cout << "Time take is " << timer.getInterval() << std::endl; And I found the speed of insertion is a little bit slow, about 20K+ records per second... Is there anyway to enhance this insertion speed? My data was in Oracle, properly indexed, so there should be no danger of corrupted data structure. I knew that in oracle you can first load then build index to save time, can I do the same with MultiIndexContainer, if there is a way? By the way, the parallel query speed is quite satisfactory, querying all the 36 m records on a 4 cpu(8kernal) machine takes only 2.8 seconds, code as below #pragma omp parallel for collapse(2) for (int i = 0; i < numOfDays_; i++) { for (int j = 0; j < accountSize_; j++) { const int& date = dates_[i]; const std::string& accountID = accountIDs_[j]; for (int k = 0; k < instSize_; k++) { const std::string& instID = instIDs_[i]; PositionSummaryContainer::iterator it = uniqueIndex.find(boost::make_tuple(date, accountID, instID)); if (it != uniqueIndex.end()) { #pragma omp atomic sum2 += (*it)->marketvalue(); } } //std::cout << "accountID: " << accountID << std::endl; } }