![]() Updating with FreshClam from existing databases set does not use much data.Ĭlamav/clamav:_base: A release with no signature databases. Use this if you will keep the image around so that you don't download the entire database set every time you start a new container. Using this container will save the ClamAV project some bandwidth. The official images on Docker HubĬlamAV image tags on Docker Hub follow these naming conventions.Ĭlamav/clamav:: A release preloaded with signature databases. The downside here is a risk that a download may fail in an unexpected way and that freshclam will unknowingly keep the broken database, causing clamd to fail to load/reload the broken file. You can disable freshclam database load testing to minimize RAM usage by setting TestDatabases no in nf. The downside is that clamd will block any new scans until reload is complete. You can minimize clamd RAM usage by setting ConcurrentDatabaseReload no in nf. What can I do to minimize RAM usage? clamd reload memory usage If you're observing issues with ClamAV failing or becoming unresponsive once a day, it is likely that your system does not have enough RAM to run ClamAV. Within Docker, this may cause your container to become unresponsive. ![]() If your container does not have enough RAM you can expect that the OS (or Docker) may kill your clamd process. It won't use quite as much as a clamd database reload, but it may still be enough to cause issues on some systems. The freshclam process may also consume a sizeable chunk of memory when load-testing newly downloaded databases. As a consequence, clamd will use twice the amount of RAM for a brief period. This process is called "concurrent reloading" and enables scans to continue during the reload. Once loaded and once all scans that use the old engine have completed, the old engine is unloaded. When the clamd processs reloads the databases after an update, the default behavior is for ClamAV to build a new engine based on the updated signatures first. This does not take into account any RAM required to process the files during the scanning process.ĬlamAV uses upwards of 2.4 GiB of RAM for a short period each day when loading new signature definitions. Whether you're using the official ClamAV docker images or third party images that run ClamAV, you will need to ensure that you have enough RAM.ĬlamAV uses upwards of 1.2 GiB of RAM simply to load the signature definitions into matching structures in the construct we call an "engine". If new or unfamiliar with Docker, containers or cgroups see. This provides isolation from other processes by running it in a containerized environment. ClamAV Versions and Functionality LevelsĬlamAV can be run within a Docker container. Microsoft Authenticode Signature Verification Selecting the Right Version of ClamAV for You
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