The state surrounding the use of IndexedDB is in a provider. There are also hooks that components can call to get action functions for operating on queries, as well as reserving a view of a range of rows in the query. We developed a RowStore class that manages the row database and caches of rows for each query. The database is very simple, it just contains the rows, keyed by the queryId and the row index.
Writing rows can take time, so we want to display rows right away. They go right into the cache, and then are written out in the background. The code keeps track of the ranges of rows that are in memory, as well as the rows that are desired to be in memory. We call the second type "retained" ranges. They are retained either because there is a spreadsheet open on the screen looking at them, or because there is a write operation pending on them.
When there are retained rows that are not actually in memory, we begin a load from indexeddb. When there are rows in memory that are not retained, we can free them, or evict them from the cache.
Previously our streaming query APIs between the main process and the render process would trigger data events as quickly as we get rows from the database. We needed a way of slowing down the query to match the rate that the results can be processed.
Otherwise we have no option but to let the rows buffer in memory, which will cause the memory usage of the app to spike until the render process is able to work its way through the backlog. For the web app, the way we were using socketIO for queries was not conducive to the ACK messages needed to prevent those memory spikes.
We were actually using the broadcast messaging functionality internally, even though we were never actually sending the messages to more than one client. Now, instead of having the query API be a normal REST endpoint that then triggers broadcast messages on websockets, we now do the entire query through the socket.
This is a cleaner design in the long run. Managing thousands of rows across cache, memory, writes, and evictions in this new way led to some interesting bugs during testing. In one instance, rows were being swapped out of memory even when visible on the user's screen. This only seemed to occur when the React debugging code was turned off. Our hypothesis was that this made the renders happen faster. In the IndexedDB code we keep rows in memory if it's a member of a range that some code in the application is requesting to be kept.
When you are looking at a table in query view, the query view should be requesting that those rows be kept. But in this case they were removed from memory and eventually read back in.
When rows in the results pane were being removed and recreated instead of updated they could be dropped in between. In the process of fixing that we found a second bug that was causing us to retain all the rows instead of just the requested ones! This made our memory issue much worse and more persistent. Now once the rows are done writing they will stop taking up memory as is intended.
Fixing both these bugs was trivial. We now no longer retain everything. We were destroying then recreating ranges instead of updating them which lead to rows being dropped from memory when they should not have been. Finally we wait for one IndexedDB transaction to finish before initiating another. This allows us to drop ranges from memory sooner after writing them. That's it. Reconfigurable reflectarrays and array lenses for dynamic antenna beam control: A review.
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Shi, Y. Riel, M. Design of an electronically beam scanning reflectarray using aperture-coupled elements. Download references. You can also search for this author in PubMed Google Scholar. The Manuscript is reviewed and approved by Z. Correspondence to Zahra Atlasbaf. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
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