growt (GrowTable) is a header only library implementing a concurrent growing hash table. There are different variants depending on the use case. See explanation below.

Using the supplied cmake build system you can compile an example file (example/example.cpp) and a multitude of different tests/benchmarks. These were also used to generate the plots displayed in our scientific publications => TOPC journal technical report (outdated).

• complex data-types (arbitrary keys and values)
  • these tables work by allocating elements on the heap, but minimizing key-comparisons
  • new emplace/move functionality
• new table dispatcher simplifies choosing the correct table

To remain fast while storing some per thread data, we use handles. These help us to remain fast when accessing the hash table. Even though this is a change from the traditional interface, we try to remain as close to std::unordered_map as possible (within the handle). To achieve this we implemented an iterator based interface. Our iterators remain secure even when the table is migrated in the background. This is necessary because in concurrent scenarios one can never be certain, when a table migration occurs.

All our hash tables support the following functionality:

called on global object

• HashTable(size_t n) - constructor allocating a table large enough to hold n elements.
• HashTable::Handle getHandle() - create handles that can be used to access the table (alternatively HashTable::Handle(HashTable global_table_obj))

called through handles within the handles, we try to keep the interface as close to std::unordered_map as possible:

• std::pair(iterator, bool) insert(uint64_t k, uint64_t d) - if no element with key k was present, insert stores the data element d at key k and returns an iterator (and true). Otherwise it returns an iterator to the already present element and false.
• std::pair(iterator, bool) update(uint64_t k, UpdateFunction f, <parameters for f>...) - looks for an element with key k. If present, the stored data is changed atomically using the supplied update function (interface/instruction below). The returned iterator points to the found element (or end()), and the bool shows if an element was found.
• std::pair(iterator, bool) insertOrUpdate(uint64_t k, uint64_t d, UpdateFunction f) - combines the two functions above. If no data was present with key k, then d is inserted, otherwise the stored data is updated. The bool can be used to find out which happened (true -> insertion, false -> update).
• size_t erase(uint64_t k) - removes the stored key value pair, returns the number of erased entries (at most one). The memory is reclaimed once the table is migrated.
• iterator find(uint64_t k) - finds the data element stored at key k and returns an iterator (end() if unfound).
• const_iterator find(uint64_t k) const - same as find

Using handles is not necessary for our non-growing tables.

called through iterators

• dereferencing/reading the values will return the values that were in the table when the iterator was created.
• refresh will update the iterator to the current state of the table. This might make the iterator end() (cannot be dereferenced) if the element was erased in the meantime.

called through a reference

• reading the values will return the values that were in the table when the iterator was created (that was dereferenced to become this reference).

Our update interface uses a user implemented update function. Any object given as an update function should have an operator()(uint64_t& cur_data, uint64_t key, uint64_t new_data) returning a uint64_t. This function will usually be called by our update operations to change a stored elements data field. This operations does not have to be atomic: it is made atomic using CAS operations. When an atomic version of the method exists, then it can be implemented as an additional function called .atomic(uint64_t& cur_data, uint64_t key, uint64_t new_data), which is only called iff atomics can be called safely (mostly in synchronized growing variants usGrow and psGrow). Presensce of the atomic variant is detected automatically. An example for a fully implemented UpdateFunction might look like this:

struct Increment
{
    uint64_t operator()(uint64_t& lhs, const uint64_t, const uint64_t rhs) const
    {
        return lhs += rhs;
    }

    // an atomic implementation can improve the performance of updates in .sGrow
    // this will be detected automatically
    uint64_t atomic    (uint64_t& lhs, const uint64_t, const uint64_t rhs) const
    {
        __sync_fetch_and_add(&lhs, rhs);
    }

};

This package contains many different concurrent hash table variants that can be accessed through a dispatcher that automates choosing the correct implementation according to a set of parameters.

All our data structures and connected classes are declared in the growt namespace.

• folklore is a simple linear probing hash table using atomic operations, to change cell contents.

Our growing variants use the above non-growing tables. They grow by migrating the entire hash table once it gets too full for the current size. Migration is done in the background without the user knowing about it. During the migration hash table accesses may be delayed until the table is migrated (usually the waiting thread will help with the migration).

Threads can only access our growing hash tables by creating a thread specific handle. These handles cannot be shared between threads.

• uaGrow is a growing table, where threads that access the table are responsible for eventual migrations. These will be performed automatically and asynchronously. Migrated cells are marked to ensure atomicity (this reduces the available key space by one bit. Keys >=2^63 cannot be inserted).
• usGrow similar to uaGrow but growing steps are somewhat synchronized (ensures automatically that no updates run during growing phases) eliminating the need for marking.
• paGrow where growing is done by a dedicated pool of growing threads. Similar to uaGrow marking is used to ensure atomicity of the hash table migration.
• psGrow combining the thread pool of paGrow with the synchronized growing approach of usGrow.

All generated tests (make recipes) have the same name structure.

<test_abbrv>_<growing_indicator>_<hash_table_name> => e.g. ins_full_uaGrow

• ins - insertion and find test (seperate)
• mix - mixed inserts and finds
• agg - aggregation using insertOrUpdate on a skewed key sequence
• con - updates and finds on a skewed key sequence
• del - alternating inserts and deletions (approx. constant table size)

Some of the following tables have to be activated through cmake options.

• sequential - our sequential table (use only one thread!)
• folklore - our non growing tables
• uaGrow, usGrow, paGrow, psGrow - our main growing tables with different growing strategies
• junction_linear, junction_grampa, junction_leap, folly, cuckoo, tbb_hm, tbb_um - third party tables

Note: in the paper we have some additional third party hash tables. These depend on some additional wrappers and are not reproduced here. Wrappers for their libraries can be found in a branch called legacy_wrappers.

To make it easy, you can include the header data-structures/table_config.hpp, which includes all necessary files and offers an interface to choose the right hash table for your workload. Additional hash table modifications can be found in data-structures/hash_table_mods.hpp

#include "data-structures/table_config.hpp"
using table_type =  typename growt::table_config<key_type, mapped_type,
                                                 hash_function, allocator_type,
                                                 // any number of hash mods
                                                 hmod::growable,
                                                 hmod::deletions>::table_type;

The utility functions are now placed in their own submodule github repository

This package can be used all on its own (see example.cpp and …test.cpp). However third party codes are used for additional functionality/tests. Most of the third party libraries are either searched on your machine (TBB, pthread), or they are placed in submodules (downloaded through git).

• TBB - to implement a fixed memory pool allocator
• xxHash - usable hash function

• TBB - tbb::concurrent_hash_map and tbb::concurrent_unordered_map
• LibCuckoo - cuckoohash_map
• Junction - junction::ConcurrentMap_Linear ..._Grampa ..._Leapfrog
• Folly - folly::AtomicHashMap

Tested using current versions of g++.

git clone https://github.com/TooBiased/growt.git
cd growt
mkdir build
cd build
cmake ..
make

Third party libraries are either installed using your package manager or they are downloaded into the misc/submodules folder.

git clone https://github.com/TooBiased/growt.git
cd growt
git submodule init
mkdir build
cd build
cmake -D GROWT_BUILD_ALL_THIRD_PARTIES=ON ..
make

note that folly needs quite a lot of extern libraries (zstd, glog, …) those have to be installed, to compile any test using folly (checkout their github https://github.com/facebook/folly).