# Multi-threading

## GDAL API: re-entrant, but (generally) not thread-safe

The exact meaning of the terms `thread-safe` or `re-entrant` is not fully standardized. We will use here the QT definitions. In particular, a C function or C++ method is said to be re-entrant if it can be called simultaneously from multiple threads, but only if each invocation uses its own data.

All GDAL public C functions and C++ methods are re-entrant, except:

- the general initialization functions, like `GDALAllRegister()`.

- the general cleanup functions like `GDALDestroy()` or `OSRCleanup()`.

Those functions should not be called concurrently from several threads, and it is general best practice to call them from the main thread of the program at program initialization and termination.

Unless otherwise stated, no GDAL public C functions and C++ methods should be assumed to be thread-safe. That is you should not call simultaneously GDAL functions from multiple threads on the same data instance, or even instances that are closely related through ownership relationships. For example, for a multi-band raster dataset, it is not safe to call concurrently GDAL functions on different `GDALRasterBand` instances owned by the same `GDALDataset` instance (each thread should instead manipulate a distinct GDALDataset). Similarly for a GDALDataset owning several `OGRLayer`.

The reason is that most implementations of GDALDataset or GDALRasterBand are stateful. A GDALDataset typically owns a file handle, and performs seek/read operations on it, thus not allowing concurrent access. Block cache related structures for a given GDALDataset are not thread-safe. Drivers also often implement lazy initialization strategies to access various metadata which are resolved only the first time the method to access them is invoked. Drivers may also rely on third-party libraries that expose objects that are not thread-safe.

Those restrictions apply to the C and C++ ABI, and all languages bindings (unless they would take special precautions to serialize calls)

## Thread-safe GDAL dataset instances for raster read-only use cases

Added in version 3.10.

RFC 101: Raster dataset read-only thread-safety adds a new capability to open, or obtain, a thread-safe dataset from any dataset, but only for raster read-only use cases.

At open time, this can be done by passing `GDAL_OF_RASTER | GDAL_OF_THREAD_SAFE` to `GDALOpenEx()` / `GDALDataset::Open()`.

Given an existing GDALDataset* instance, `GDALDataset::IsThreadSafe()` can be used to determine if it is thread-safe or not. If not, `GDALGetThreadSafeDataset()` can be used.

Note that the generic implementation of this capability involves opening one dataset the first time a thread-safe dataset/raster band is accessed by a thread. While this is an implementation detail that can be ignored to develop code, it is important to note regarding potential performance impacts

## GDAL block cache and multi-threading

The current design of the GDAL raster block cache allows concurrent reads of several datasets. However performance issues may arise when writing several datasets from several threads, due to lock contention in the global structures of the block cache mechanism.

## RAM fragmentation and multi-threading

It has been observed that scenarios that involve multi-threading reading or writing of raster datasets are prone to cause a high RAM usage, in particular when using the default dynamic memory allocator of Linux. Using the alternate tcmalloc memory allocator helps reducing the amount of virtual and resident memory used.

For example, with Debian/Ubuntu distributions, this can be done by installing the `libtcmalloc-minimal4` package and running the binary that executes GDAL with:

    LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4 ./binary

## GDAL and multi-processing

POSIX fork() API should not be called during the middle of a GDAL operation, otherwise some structures like mutexes might appear to be locked forever in the forked process. If multi-processing is done, we recommend that processes are forked before any GDAL operation is done. Operating on the same GDALDataset instance in several sub-processes will generally lead to wrong results due to the underlying file descriptors being shared.