Geoprocessing is a core function of ESRI software that allows you to manipulate, analyze, and transform spatial data. However, geoprocessing can also be a source of frustration and inefficiency if you encounter common performance pitfalls. In this article, you will learn what are some of these pitfalls and how can you avoid them in ESRI.
Not all geoprocessing tools are created equal. Some tools are faster, more scalable, or more suitable for certain types of data than others. For example, using the Clip tool to extract a subset of features from a large dataset can be very slow and memory-intensive, while using the Select tool with a spatial query can be much faster and lighter. Therefore, you should always check the tool reference and documentation to find the best tool for your task and data.
The quality, format, and location of your data can have a significant impact on your geoprocessing performance. For example, using feature classes in a geodatabase can be faster than using shapefiles, because geodatabases support spatial indexing and compression. Similarly, using local data can be faster than using data on a network drive or a web service, because it reduces the network latency and bandwidth issues. Therefore, you should always optimize your data by choosing the appropriate format, projection, resolution, and storage.
Some geoprocessing tools can take advantage of parallel processing, which means they can use multiple CPU cores or threads to execute faster. For example, the Buffer tool, the Dissolve tool, and the Zonal Statistics tool can use parallel processing if you enable it in the environment settings. However, parallel processing is not always beneficial, as it can also increase the memory usage and the overhead of managing multiple processes. Therefore, you should always test and monitor your parallel processing settings to find the optimal balance.
Sometimes, you may need to perform geoprocessing tasks on multiple datasets or features. For example, you may need to clip multiple layers by a common boundary, or calculate statistics for each polygon in a feature class. However, repeating the same geoprocessing tool for each input can be very inefficient and time-consuming, as it can create intermediate outputs, consume memory, and increase the tool startup and shutdown costs. Therefore, you should always avoid unnecessary iterations by using batch processing, model builder, or scripting to automate and streamline your workflows.
One of the most common sources of geoprocessing performance degradation is the disk I/O, which means the reading and writing of data from and to the disk. Disk I/O can be slow and bottleneck your geoprocessing operations, especially if you have large or complex datasets, or if you use multiple intermediate outputs. Therefore, you should always use in-memory workspace, which means storing your temporary data in the RAM instead of the disk. You can use in-memory workspace by specifying "in_memory" as the output path or workspace in your geoprocessing tools.
Even if you follow the best practices and tips for geoprocessing performance optimization, you may still encounter some issues or errors that affect your results or speed. For example, you may run out of memory, encounter a tool failure, or get unexpected outputs. Therefore, you should always monitor and troubleshoot your geoprocessing operations by using the geoprocessing history, the messages window, the progress dialog, the system monitor, and the log files. These tools can help you identify the source of the problem, fix it, or report it to ESRI support.
Remove unnecessary fields from your model to cut down on the amount of memory and disk required to run your model. The easiest way to do this is to keep an eye out for tools that include a "Field Mappings" parameter. Instead of accepting the default (which includes all your fields!) limit the results to only the fields required for your model. It will not only make that tool run faster and leaner, but all tools downstream of it will also benefit.