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These servers temporarily buffer images and write all of them to the cache storage. Online analysis servers can intercept data from the data handling servers for real-time analysis. As discussed below, low-level filtering and programs Gefitinib using the SACLA online API can be executed only on these servers. These servers constitute the online part of the SACLA DAQ system. The offline analysis is performed on SACLA HPC nodes. These nodes can access the cache storage, the metadata database (DB) and the long-term archive storage, but not the data handling servers. This separation ensures that the online system can always achieve data collection at 60?Hz, independently of the loads on the offline system. Figure 1 Architecture of the data processing environment at SACLA. The online pipeline runs on the online analysis server. Eight image acquisition threads retrieve image data from the corresponding data handling servers and fill the frame buffer (shown by arrows). ... Image data are accessible through the SACLA API. Metadata, such as shot-by-shot spectra and photodiode readouts in pump�Cprobe experiments, are also available through the API. Metadata are associated with an image Dasatinib clinical trial by a tag number, a unique 64?bit serial number associated with each XFEL pulse. The API comes in two versions: online API and offline API. The online API is used to intercept data from memory on the data handling servers. All detector frames can be extracted at 60?Hz with a latency of a few tens of milliseconds. The online API is available only at the online analysis servers, which have direct connections to the data handling servers. The online analysis server has limited output bandwidth (Oxygenase depending on the time after data collection. Data access is encapsulated by the API, so user programs (including this pipeline) are unaware of the actual location. The offline API has limited throughput (at most 10?Hz for eight sensors per single thread) owing to IO bottlenecks. Since the offline API can only read images from completed runs, a run must finish before processing. This imposes a latency of about three minutes (time to collect 5150 images in a run at 30?Hz). 3.?Online and offline pipelines ? Since the online and offline APIs have specific limitations, we run data processing in two stages (Fig. 2 ?). The first stage, the online pipeline, is based on the online API. The purpose is to run spot finding on all images by using Cheetah, which provides real-time feedback on hit rates and detector saturation.