APEX: A High-Performance Learned Index on Persistent Memory

More details are described in our preprint.

Building

Dependencies

We tested our build with Linux Kernel 5.10.11 and GCC 10.2.0. You must ensure that your Linux kernel version >= 4.17 and glibc >=2.29 for proper build.

Compiling

Assuming to compile under a build directory:

git clone https://github.com/baotonglu/apex.git
cd apex
./build.sh

Running benchmark

Persistent memory pool path

Please ensure your PM device is properly configured with App Direct mode and mounted to file system with "DAX" enabled. Change the PM pool path of our allocator to the memory path on your own server before testing.

Benchmark setting

We run the tests in a single NUMA node with 24 physical CPU cores. We pin threads to physical cores compactly assuming thread ID == 2 * core ID (e.g., for a dual-socket system, we assume cores 0, 2, 4, ... are located in socket 0). Check out also the total.sh and run.sh script for example benchmarks and easy testing of the index. It supports the following arguments:

./build/benchmark [OPTION...]

--keys_file               the name of the dataset
--keys_file_type          the reading method for dataset (binary/text/sosd)
--keys_type               the type of the key (double/uint64)
--total_num_keys          total number of keys in the dataset
--init_num_keys           the number of keys to bulk-load before testing
--workload_keys           the number of keys in the workload
--operation               the query type in the workload (insert/search/erase/update/range/mixed)
--insert_frac             the fraction of insert in mixed search-insert workload
--lookup_distribution     the access distribution of the workload (uniform/zipf)
--theta                   the skewness of zipf (e.g.,0.9)
--using_epoch             whether to register epoch in application level: 0/1 
--thread_num              the number of worker threads 
--index                   the name of index to evaluate (apex)
--random_shuffle          whether to do the random shuffle for the dataset
--sort_bulkload           whether sort the keys before bulk-loading

Competitors

Here hosts source codes which are used in comparision with APEX , including LB+-Tree [1], DPTree [2], uTree [3], FPTree [4], BzTree [5] and FAST+FAIR [6].

[1] https://github.com/schencoding/lbtree
[2] https://github.com/zxjcarrot/DPTree-code
[3] https://github.com/thustorage/nvm-datastructure
[4] https://github.com/sfu-dis/fptree
[5] https://github.com/sfu-dis/bztree
[6] https://github.com/DICL/FAST_FAIR

Datasets

• Longitudes (200M 8-byte floats)
• Longlat (200M 8-byte floats)
• Lognormal (190M 8-byte ints)
• YCSB (200M 8-byte ints)
• FB (200M 8-byte ints)
• TPCE (259M 8-byte ints)

Acknowledgements

Our implementation is based on the code of ALEX.