libmdbx ======= _libmdbx_ is an extremely fast, compact, powerful, embedded, transactional [key-value store](https://en.wikipedia.org/wiki/Key-value_database) database, with [permissive license](LICENSE). _MDBX_ has a specific set of properties and capabilities, focused on creating unique lightweight solutions with extraordinary performance. 1. Allows **swarm of multi-threaded processes to [ACID]((https://en.wikipedia.org/wiki/ACID))ly read and update** several key-value [maps](https://en.wikipedia.org/wiki/Associative_array) and [multimaps](https://en.wikipedia.org/wiki/Multimap) in a localy-shared database. 2. Provides **extraordinary performance**, minimal overhead through [Memory-Mapping](https://en.wikipedia.org/wiki/Memory-mapped_file) and `Olog(N)` operations costs by virtue of [B+ tree](https://en.wikipedia.org/wiki/B%2B_tree). 3. Requires **no maintenance and no crash recovery** since doesn't use [WAL](https://en.wikipedia.org/wiki/Write-ahead_logging), but that might be a caveat for write-intensive workloads. 4. **Compact and friendly for fully embeddeding**. Only 25KLOC of `C11`, 64K x86 binary code, no internal threads neither processes, but implements a simplified variant of the [Berkeley DB](https://en.wikipedia.org/wiki/Berkeley_DB) and [dbm](https://en.wikipedia.org/wiki/DBM_(computing)) API. 5. Enforces [serializability](https://en.wikipedia.org/wiki/Serializability) for writers just by single [mutex](https://en.wikipedia.org/wiki/Mutual_exclusion) and affords [wait-free](https://en.wikipedia.org/wiki/Non-blocking_algorithm#Wait-freedom) for parallel readers without atomic/interlocked operations, while **writing and reading transactions do not block each other**. 6. **Guarantee data integrity** after crash unless this was explicitly neglected in favour of write performance. 7. Supports Linux, Windows, MacOS, FreeBSD, DragonFly, Solaris, OpenSolaris, OpenIndiana, NetBSD, OpenBSD and other systems compliant with **POSIX.1-2008**. Historically, _MDBX_ is deeply revised and extended descendant of amazing [Lightning Memory-Mapped Database](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database). _MDBX_ inherits all benefits from _LMDB_, but resolves some issues and adds set of improvements. The next version is under active non-public development from scratch and will be released as **_MithrilDB_** and `libmithrildb` for libraries & packages. Admittedly mythical [Mithril](https://en.wikipedia.org/wiki/Mithril) is resembling silver but being stronger and lighter than steel. Therefore _MithrilDB_ is rightly relevant name. > _MithrilDB_ will be radically different from _libmdbx_ by the new > database format and API based on C++17, as well as the [Apache 2.0 > License](https://www.apache.org/licenses/LICENSE-2.0). The goal of this > revolution is to provide a clearer and robust API, add more features and > new valuable properties of database. [](https://travis-ci.org/erthink/libmdbx) [](https://ci.appveyor.com/project/erthink/libmdbx/branch/master) [](https://scan.coverity.com/projects/reopen-libmdbx) *The Future will (be) [Positive](https://www.ptsecurity.com). Всё будет хорошо.* ----- ## Table of Contents - [Overview](#overview) - [Features](#features) - [Limitations](#limitations) - [Caveats & Gotchas](#caveats--gotchas) - [Comparison with other databases](#comparison-with-other-databases) - [Improvements beyond LMDB](#improvements-beyond-lmdb) - [History & Acknowledgments](#history) - [Usage](#usage) - [Building](#building) - [API description](#api-description) - [Bindings](#bindings) - [Performance comparison](#performance-comparison) - [Integral performance](#integral-performance) - [Read scalability](#read-scalability) - [Sync-write mode](#sync-write-mode) - [Lazy-write mode](#lazy-write-mode) - [Async-write mode](#async-write-mode) - [Cost comparison](#cost-comparison) # Overview ## Features - Key-value data model, keys are always sorted. - Fully [ACID](https://en.wikipedia.org/wiki/ACID)-compliant, through to [MVCC](https://en.wikipedia.org/wiki/Multiversion_concurrency_control) and [CoW](https://en.wikipedia.org/wiki/Copy-on-write). - Multiple key-value sub-databases within a single datafile. - Range lookups, including range query estimation. - Efficient support for short fixed length keys, including native 32/64-bit integers. - Ultra-efficient support for [multimaps](https://en.wikipedia.org/wiki/Multimap). Multi-values sorted, searchable and iterable. Keys stored without duplication. - Data is [memory-mapped](https://en.wikipedia.org/wiki/Memory-mapped_file) and accessible directly/zero-copy. Traversal of database records is extremely-fast. - Transactions for readers and writers, ones do not block others. - Writes are strongly serialized. No transactions conflicts nor deadlocks. - Readers are [non-blocking](https://en.wikipedia.org/wiki/Non-blocking_algorithm), notwithstanding [snapshot isolation](https://en.wikipedia.org/wiki/Snapshot_isolation). - Nested write transactions. - Reads scales linearly across CPUs. - Continuous zero-overhead database compactification. - Automatic on-the-fly database size adjustment. - Customizable database page size. - `Olog(N)` cost of lookup, insert, update, and delete operations by virtue of [B+ tree characteristics](https://en.wikipedia.org/wiki/B%2B_tree#Characteristics). - Online hot backup. - Append operation for efficient bulk insertion of pre-sorted data. - No [WAL](https://en.wikipedia.org/wiki/Write-ahead_logging) nor any transaction journal. No crash recovery needed. No maintenance is required. - No internal cache and/or memory management, all done by basic OS services. ## Limitations - **Page size**: a power of 2, maximum `65536` bytes, default `4096` bytes. - **Key size**: minimum 0, maximum ≈¼ pagesize (`1300` bytes for default 4K pagesize, `21780` bytes for 64K pagesize). - **Value size**: minimum 0, maximum `2146435072` (`0x7FF00000`) bytes for maps, ≈¼ pagesize for multimaps (`1348` bytes default 4K pagesize, `21828` bytes for 64K pagesize). - **Write transaction size**: up to `4194301` (`0x3FFFFD`) pages (16 [GiB](https://en.wikipedia.org/wiki/Gibibyte) for default 4K pagesize, 256 [GiB](https://en.wikipedia.org/wiki/Gibibyte) for 64K pagesize). - **Database size**: up to `2147483648` pages (8 [TiB](https://en.wikipedia.org/wiki/Tebibyte) for default 4K pagesize, 128 [TiB](https://en.wikipedia.org/wiki/Tebibyte) for 64K pagesize). - **Maximum sub-databases**: `32765`. ## Caveats & Gotchas 1. There cannot be more than one writer at a time, i.e. no more than one write transaction at a time. 2. MDBX is based on [B+ tree](https://en.wikipedia.org/wiki/B%2B_tree), so access to database pages is mostly random. Thus SSDs provide a significant performance boost over spinning disks for large databases. 3. MDBX uses [shadow paging](https://en.wikipedia.org/wiki/Shadow_paging) instead of [WAL](https://en.wikipedia.org/wiki/Write-ahead_logging). Thus syncing data to disk might be bottleneck for write intensive workload. 4. MDBX uses [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write) for [snapshot isolation](https://en.wikipedia.org/wiki/Snapshot_isolation) during updates, but read transactions prevents recycling an old retired/freed pages, since it read ones. Thus altering of data during a parallel long-lived read operation will increase the process work set, may exhaust entire free database space, the database can grow quickly, and result in performance degradation. Try to avoid long running read transactions. 5. MDBX is extraordinarily fast and provides minimal overhead for data access, so you should reconsider about use brute force techniques and double check your code. On the one hand, in the case of MDBX, a simple linear search may be more profitable than complex indexes. On the other hand, if you make something suboptimally, you can notice a detrimentally only on sufficiently large data. ### Comparison with other databases For now please refer to [chapter of "BoltDB comparison with other databases"](https://github.com/coreos/bbolt#comparison-with-other-databases) which is also (mostly) applicable to _libmdbx_. Improvements beyond LMDB ======================== _libmdbx_ is superior to legendary _[LMDB](https://symas.com/lmdb/)_ in terms of features and reliability, not inferior in performance. In comparison to _LMDB_, _libmdbx_ make things "just work" perfectly and out-of-the-box, not silently and catastrophically break down. The list below is pruned down to the improvements most notable and obvious from the user's point of view. ### Added Features: 1. Keys could be more than 2 times longer than _LMDB_. > For DB with default page size _libmdbx_ support keys up to 1300 bytes > and up to 21780 bytes for 64K page size. _LMDB_ allows key size up to > 511 bytes and may silently loses data with large values. 2. Up to 20% faster than _LMDB_ in [CRUD](https://en.wikipedia.org/wiki/Create,_read,_update_and_delete) benchmarks. > Benchmarks of the in-[tmpfs](https://en.wikipedia.org/wiki/Tmpfs) scenarios, > that tests the speed of engine itself, shown that _libmdbx_ 10-20% faster than _LMDB_. > These and other results could be easily reproduced with [ioArena](https://github.com/pmwkaa/ioarena) just by `make bench-quartet`, > including comparisons with [RockDB](https://en.wikipedia.org/wiki/RocksDB) > and [WiredTiger](https://en.wikipedia.org/wiki/WiredTiger). 3. Automatic on-the-fly database size adjustment, both increment and reduction. > _libmdbx_ manage the database size according to parameters specified > by `mdbx_env_set_geometry()` function, > ones include the growth step and the truncation threshold. 4. Automatic continuous zero-overhead database compactification. > During each commit _libmdbx_ merges suitable freeing pages into unallocated area > at the end of file, and then truncate unused space when a lot enough of. 5. The same database format for 32- and 64-bit builds. > _libmdbx_ database format depends only on the [endianness](https://en.wikipedia.org/wiki/Endianness) but not on the [bitness](https://en.wiktionary.org/wiki/bitness). 6. LIFO policy for Garbage Collection recycling. This can significantly increase write performance due write-back disk cache up to several times in a best case scenario. > LIFO means that for reuse will be taken latest became unused pages. > Therefore the loop of database pages circulation becomes as short as possible. > In other words, the set of pages, that are (over)written in memory and on disk during a series of write transactions, will be as small as possible. > Thus creates ideal conditions for the battery-backed or flash-backed disk cache efficiency. 7. Fast estimation of range query result volume, i.e. how many items can be found between a `KEY1` and a `KEY2`. This is prerequisite for build and/or optimize query execution plans. > _libmdbx_ performs a rough estimate based on common B-tree pages of the paths from root to corresponding keys. 8. `mdbx_chk` tool for database integrity check. 9. Automated steady sync-to-disk upon several thresholds and/or timeout via cheap polling. 10. Sequence generation and three persistent 64-bit markers. 11. Callback for lack-of-space condition of database that allows you to control and/or resolve such situations. 12. Support for opening database in the exclusive mode, including on a network share. ### Added Abilities: 1. Zero-length for keys and values. 2. Ability to determine whether the particular data is on a dirty page or not, that allows to avoid copy-out before updates. 3. Ability to determine whether the cursor is pointed to a key-value pair, to the first, to the last, or not set to anything. 4. Extended information of whole-database, sub-databases, transactions, readers enumeration. > _libmdbx_ provides a lot of information, including dirty and leftover pages > for a write transaction, reading lag and holdover space for read transactions. 5. Extended update and delete operations. > _libmdbx_ allows ones _at once_ with getting previous value > and addressing the particular item from multi-value with the same key. ### Other fixes and specifics: 1. Fixed more than 10 significant errors, in particular: page leaks, wrong sub-database statistics, segfault in several conditions, unoptimal page merge strategy, updating an existing record with a change in data size (including for multimap), etc. 2. All cursors can be reused and should be closed explicitly, regardless ones were opened within write or read transaction. 3. Opening database handles are spared from race conditions and pre-opening is not needed. 4. Returning `MDBX_EMULTIVAL` error in case of ambiguous update or delete. 5. Guarantee of database integrity even in asynchronous unordered write-to-disk mode. > _libmdbx_ propose additional trade-off by implementing append-like manner for updates > in `MDBX_SAFE_NOSYNC` and `MDBX_WRITEMAP|MDBX_MAPASYNC` modes, that avoid database corruption after a system crash > contrary to LMDB. Nevertheless, the `MDBX_UTTERLY_NOSYNC` mode available to match LMDB behaviour, > and for a special use-cases. 6. On **MacOS** the `fcntl(F_FULLFSYNC)` syscall is used _by default_ to synchronize data with the disk, as this is [the only way to guarantee data durability](https://developer.apple.com/library/archive/documentation/System/Conceptual/ManPages_iPhoneOS/man2/fsync.2.html) in case of power failure. Unfortunately, in scenarios with high write intensity, the use of `F_FULLFSYNC` significant degrades performance compared to LMDB, where the `fsync()` syscall is used. Therefore, _libmdbx_ allows you to override this behavior by defining the `MDBX_OSX_SPEED_INSTEADOF_DURABILITY=1` option while build the library. 7. On **Windows** the `LockFileEx()` syscall is used for locking, since it allows place the database on network drives, and provides protection against incompetent user actions (aka [poka-yoke](https://en.wikipedia.org/wiki/Poka-yoke)). Therefore _libmdbx_ may be a little lag in performance tests from LMDB where a named mutexes are used. ### History At first the development was carried out within the [ReOpenLDAP](https://github.com/erthink/ReOpenLDAP) project. About a year later _libmdbx_ was separated into standalone project, which was [presented at Highload++ 2015 conference](http://www.highload.ru/2015/abstracts/1831.html). Since 2017 _libmdbx_ is used in [Fast Positive Tables](https://github.com/erthink/libfpta), and development is funded by [Positive Technologies](https://www.ptsecurity.com). ### Acknowledgments Howard Chu is the author of LMDB, from which originated the MDBX in 2015. Martin Hedenfalk is the author of `btree.c` code, which was used for begin development of LMDB. -------------------------------------------------------------------------------- Usage ===== ## Source code embedding _libmdbx_ provides two official ways for integration in source code form: 1. Using the amalgamated source code. > The amalgamated source code includes all files requires to build and > use _libmdbx_, but not for testing _libmdbx_ itself. 2. Adding the complete original source code as a `git submodule`. > This allows you to build as _libmdbx_ and testing tool. > On the other hand, this way requires you to pull git tags, and use C++11 compiler for test tool. **_Please, avoid using any other techniques._** Otherwise, at least don't ask for support and don't name such chimeras `libmdbx`. The amalgamated source code could be created from original clone of git repository on Linux by executing `make dist`. As a result, the desired set of files will be formed in the `dist` subdirectory. ## Building Both amalgamated and original source code provides build through the use [CMake](https://cmake.org/) or [GNU Make](https://www.gnu.org/software/make/) with [bash](https://en.wikipedia.org/wiki/Bash_(Unix_shell)). All build ways are completely traditional and have minimal prerequirements like `build-essential`, i.e. the non-obsolete C/C++ compiler and a [SDK](https://en.wikipedia.org/wiki/Software_development_kit) for the target platform. Obviously you need building tools itself, i.e. `git`, `cmake` or GNU `make` with `bash`. So just use CMake or GNU Make in your habitual manner and feel free to fill an issue or make pull request in the case something will be unexpected or broken down. #### DSO/DLL unloading and destructors of Thread-Local-Storage objects When building _libmdbx_ as a shared library or use static _libmdbx_ as a part of another dynamic library, it is advisable to make sure that your system ensures the correctness of the call destructors of Thread-Local-Storage objects when unloading dynamic libraries. If this is not the case, then unloading a dynamic-link library with _libmdbx_ code inside, can result in either a resource leak or a crash due to calling destructors from an already unloaded DSO/DLL object. The problem can only manifest in a multithreaded application, which makes the unloading of shared dynamic libraries with _libmdbx_ code inside, after using _libmdbx_. It is known that TLS-destructors are properly maintained in the following cases: - On all modern versions of Windows (Windows 7 and later). - On systems with the [`__cxa_thread_atexit_impl()`](https://sourceware.org/glibc/wiki/Destructor%20support%20for%20thread_local%20variables) function in the standard C library, including systems with GNU libc version 2.18 and later. - On systems with libpthread/ntpl from GNU libc with bug fixes [#21031](https://sourceware.org/bugzilla/show_bug.cgi?id=21031) and [#21032](https://sourceware.org/bugzilla/show_bug.cgi?id=21032), or where there are no similar bugs in the pthreads implementation. ### Linux and other platforms with GNU Make To build the library it is enough to execute `make all` in the directory of source code, and `make check` for execute the basic tests. If the `make` installed on the system is not GNU Make, there will be a lot of errors from make when trying to build. In this case, perhaps you should use `gmake` instead of `make`, or even `gnu-make`, etc. ### FreeBSD and related platforms As a rule, in such systems, the default is to use Berkeley Make. And GNU Make is called by the gmake command or may be missing. In addition, [bash](https://en.wikipedia.org/wiki/Bash_(Unix_shell)) may be absent. You need to install the required components: GNU Make, bash, C and C++ compilers compatible with GCC or CLANG. After that, to build the library, it is enough execute `gmake all` (or `make all`) in the directory with source code, and `gmake check` (or `make check`) to run the basic tests. ### Windows For build _libmdbx_ on Windows the _original_ CMake and [Microsoft Visual Studio](https://en.wikipedia.org/wiki/Microsoft_Visual_Studio) are recommended. Building by MinGW, MSYS or Cygwin is potentially possible. However, these scripts are not tested and will probably require you to modify the CMakeLists.txt or Makefile respectively. It should be noted that in _libmdbx_ was efforts to resolve runtime dependencies from CRT and other libraries Visual Studio. For this is enough define the `MDBX_AVOID_CRT` during build. An example of running a basic test script can be found in the [CI-script](appveyor.yml) for [AppVeyor](https://www.appveyor.com/). To run the [long stochastic test scenario](test/long_stochastic.sh), [bash](https://en.wikipedia.org/wiki/Bash_(Unix_shell)) is required, and the such testing is recommended with place the test data on the [RAM-disk](https://en.wikipedia.org/wiki/RAM_drive). ### MacOS Current [native build tools](https://en.wikipedia.org/wiki/Xcode) for MacOS include GNU Make, CLANG and an outdated version of bash. Therefore, to build the library, it is enough to run `make all` in the directory with source code, and run `make check` to execute the base tests. If something goes wrong, it is recommended to install [Homebrew](https://brew.sh/) and try again. To run the [long stochastic test scenario](test/long_stochastic.sh), you will need to install the current (not outdated) version of [bash](https://en.wikipedia.org/wiki/Bash_(Unix_shell)). To do this, we recommend that you install [Homebrew](https://brew.sh/) and then execute `brew install bash`. ## API description For more information and API description see the [mdbx.h](mdbx.h) header. ## Bindings | Runtime | GitHub | Author | | -------- | ------ | ------ | | Java | [mdbxjni](https://github.com/castortech/mdbxjni) | [Castor Technologies](https://castortech.com/) | | .NET | [mdbx.NET](https://github.com/wangjia184/mdbx.NET) | [Jerry Wang](https://github.com/wangjia184) | | Rust | [mdbx-rs](https://github.com/Kerollmops/mdbx-rs) | [Clément Renault](https://github.com/Kerollmops) | -------------------------------------------------------------------------------- Performance comparison ====================== All benchmarks were done in 2015 by [IOArena](https://github.com/pmwkaa/ioarena) and multiple [scripts](https://github.com/pmwkaa/ioarena/tree/HL%2B%2B2015) runs on Lenovo Carbon-2 laptop, i7-4600U 2.1 GHz (2 physical cores, 4 HyperThreading cores), 8 Gb RAM, SSD SAMSUNG MZNTD512HAGL-000L1 (DXT23L0Q) 512 Gb. ## Integral performance Here showed sum of performance metrics in 3 benchmarks: - Read/Search on machine with 4 logical CPU in HyperThreading mode (i.e. actually 2 physical CPU cores); - Transactions with [CRUD](https://en.wikipedia.org/wiki/CRUD) operations in sync-write mode (fdatasync is called after each transaction); - Transactions with [CRUD](https://en.wikipedia.org/wiki/CRUD) operations in lazy-write mode (moment to sync data to persistent storage is decided by OS). *Reasons why asynchronous mode isn't benchmarked here:* 1. It doesn't make sense as it has to be done with DB engines, oriented for keeping data in memory e.g. [Tarantool](https://tarantool.io/), [Redis](https://redis.io/)), etc. 2. Performance gap is too high to compare in any meaningful way.  -------------------------------------------------------------------------------- ## Read Scalability Summary performance with concurrent read/search queries in 1-2-4-8 threads on 4 CPU cores machine.  -------------------------------------------------------------------------------- ## Sync-write mode - Linear scale on left and dark rectangles mean arithmetic mean transactions per second; - Logarithmic scale on right is in seconds and yellow intervals mean execution time of transactions. Each interval shows minimal and maximum execution time, cross marks standard deviation. **10,000 transactions in sync-write mode**. In case of a crash all data is consistent and state is right after last successful transaction. [fdatasync](https://linux.die.net/man/2/fdatasync) syscall is used after each write transaction in this mode. In the benchmark each transaction contains combined CRUD operations (2 inserts, 1 read, 1 update, 1 delete). Benchmark starts on empty database and after full run the database contains 10,000 small key-value records.  -------------------------------------------------------------------------------- ## Lazy-write mode - Linear scale on left and dark rectangles mean arithmetic mean of thousands transactions per second; - Logarithmic scale on right in seconds and yellow intervals mean execution time of transactions. Each interval shows minimal and maximum execution time, cross marks standard deviation. **100,000 transactions in lazy-write mode**. In case of a crash all data is consistent and state is right after one of last transactions, but transactions after it will be lost. Other DB engines use [WAL](https://en.wikipedia.org/wiki/Write-ahead_logging) or transaction journal for that, which in turn depends on order of operations in journaled filesystem. _libmdbx_ doesn't use WAL and hands I/O operations to filesystem and OS kernel (mmap). In the benchmark each transaction contains combined CRUD operations (2 inserts, 1 read, 1 update, 1 delete). Benchmark starts on empty database and after full run the database contains 100,000 small key-value records.  -------------------------------------------------------------------------------- ## Async-write mode - Linear scale on left and dark rectangles mean arithmetic mean of thousands transactions per second; - Logarithmic scale on right in seconds and yellow intervals mean execution time of transactions. Each interval shows minimal and maximum execution time, cross marks standard deviation. **1,000,000 transactions in async-write mode**. In case of a crash all data will be consistent and state will be right after one of last transactions, but lost transaction count is much higher than in lazy-write mode. All DB engines in this mode do as little writes as possible on persistent storage. _libmdbx_ uses [msync(MS_ASYNC)](https://linux.die.net/man/2/msync) in this mode. In the benchmark each transaction contains combined CRUD operations (2 inserts, 1 read, 1 update, 1 delete). Benchmark starts on empty database and after full run the database contains 10,000 small key-value records.  -------------------------------------------------------------------------------- ## Cost comparison Summary of used resources during lazy-write mode benchmarks: - Read and write IOPs; - Sum of user CPU time and sys CPU time; - Used space on persistent storage after the test and closed DB, but not waiting for the end of all internal housekeeping operations (LSM compactification, etc). _ForestDB_ is excluded because benchmark showed it's resource consumption for each resource (CPU, IOPs) much higher than other engines which prevents to meaningfully compare it with them. All benchmark data is gathered by [getrusage()](http://man7.org/linux/man-pages/man2/getrusage.2.html) syscall and by scanning data directory.  -------------------------------------------------------------------------------- #### This is a mirror of the origin repository that was moved to [abf.io](https://abf.io/erthink/) because of discriminatory restrictions for Russian Crimea.