![]() When such a crash occurs, the app is terminated - a terrible user experience. A bug in the Google Maps package occasionally results in a native crash when taking a snapshot. To do that, a snapshot of the map is taken and composed with other components. To support creating awesome weather visualizations that can be shared on social media like this one, the app has to be able to render maps to an image. For example, when scrolling vertically, the map widget is placed too high, too low, or gets stuck at the bottom of the screen. When scrolling, the platform map widget is occasionally misplaced. When platform widgets are not included in a scroll view, it works beautifully. This clever approach to layering make them appear the same as other Flutter widgets. This means that the map widget must scroll along with other content.įlutter positions native platform widgets ( platform views) over top of the Flutter view hierarchy. With mobile devices having smaller screens, scrolling is a necessity. ![]() My app presents a map with weather data and various charts. There were two issues that prevented me from using Google maps in my app: 1. This post details the issues that I encountered and how I ended up solving the problem by creaing a plug-in vector_map_tiles to add support for vector tiles to flutter_map. The platform/Flutter combination is not perfect, and for my use-case isn’t good enough for a prodution app. The resulting map looks and behaves beautifully, except when it doesn’t. Maps are commonly added using the google_maps_flutter package, which uses native platform widgets to embed a Google map in your Flutter app. ![]() One of the formats that makes use of all the mentioned optimizations is the tiled GeoTIFF format.Flutter is a huge productivity booster except for a few sharp edges, one of which is embedding maps. 256x256 or 512x512 will significantly speed up the processing time.īoth things can be done using command-line tools of the GDAL library, such as gdal_translate. A block size that is similar to the tile output size e.g. MapTiler Engine is the most effective at reading 256x256 blocks of data.įor large raster datasets, a tile-based format (as opposed to scanline-based) will drastically speed up processing. In the vast majority of cases, the overhead of the network connection and protocols for reading the input data over the network introduces a bottleneck that severely impairs processing performance.Īpart from buying more CPUs, you can also reach the speed gain by optimizing your workflow, especially for a large amount of data. Hence, avoid using HDDs, so you allow MapTiler Engine to fully utilize the potential of your machine's CPU.Īlso, processing input data from network shares is discouraged. MapTiler Engine is mainly CPU-intensive, but the storage speed comes right after it. It's highly recommended to use a fast local SSD drive to store the input data. You can check the number of CPUs available in your subscription plan as well as the number of CPUs your machine has by going to Account -> License key. To learn more, please refer to the MapTiler Engine pricing page. continents or the whole world).įor custom needs, we also offer the Enterprise plan, that can be fitted to more complex deployment scenarios. With such power, it is possible to render extremely large datasets (e.g. The Pro version is able to render on 16 CPUs. The Free version is limited to 1 CPU, and MapTiler Engine Plus has a limit of 4 CPUs for rendering. This article provides some tips that might help to improve the performance of the map rendering process. This way MapTiler Engine can provide higher performance even on a dual-core computer. The modern CPUs have multiple cores and support Hyperthreading which provides multiple logical CPUs per core. MapTiler Engine is a multi-threaded program.
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