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Cosmo run 7k full#
If you’re seeing it take a full day at 200k RUs, something is wrong or missing by an order of magnitude. There are a couple of things I can think of that would have a +-30% efficiency impact, but at 200k RU/s, it should complete in a little over an hour if the docs are all 1KB or less.
Cosmo run 7k serial#
I have a rule of thumb that a serial query (single thread, no async programming, no notable network latency, no app processing time) will consume a little over 300 RU/s, so to grow that to 12k RU/s, you’d need to have 40 commands in flight (or more, to compensate for latency, app-side processing, etc) and because it’s exceeding 10k RUs it definitely needs to parallelize across multiple partitions. If your documents are larger than 1KB, the cost will go up correspondingly. To complete it in 24 hours, you’d need to be efficiently spending about 12k RU/s. "Just for a sense of scale, updating 100M records will cost approximately 1B RUs – at least. Duration of the udpates will be a function of RUs you allow for the updates and the size of your Spark cluster (Number of cores for executors mostly) But we assume the effort to do the migration, cut-over with no/minimized downtime would outweigh the RU savings – so I would recommend going with the Spark connector, updating the documents and if necessary restricting the RUs that can be used for the updates (sample above shows that) so that your normal workloads would still work. So from a perspective of minimizing the RUs it would be “cheaper” to insert the documents into a new container. RU-wise updating a document has a higher RU charge than inserting a new document. And if you need to do it in streaming mode (because the total dataset would require too large of a Spark cluster to handle): azure-sdk-for-java/02_StructuredStreaming.ipynb at master.
Cosmo run 7k how to#
The easiest approach would be to use the Spark connector.
My question is, is there a better way to be doing this sort of transformation? Should I create a new collection, and COPY the data from one to the other? Is Cosmos perhaps not the right technology to be using when mass updates are Pliska Right now, it’s reporting that it’ll get done at tomorrow at about 4am. The cost of hitting the stored procedure when a full 500 records are updated is about 7k RU. That’s four hits on the stored procedure. Metrics says I’m maxing that out, and I’m hitting perhaps 2000 records per second…. My c# app calls this method over and over, passing a partition key for a range of records that are not yet updated. Third, I tried to modify the stored procedure to grab 500 records from the given partition, do the update, save them, and then return. With 200k RU’s provisioned I was still looking at over a week of running. This increased my though put but not enough. I loaded the record within that stored procedure, made the update, and then saved it. Second, I tried to write a cosmos stored procedure, which took the partition key and the record Id. I was using batch updates, and the throughput was poor. In this case I need to add just a single field, which will be calculated using values from other fields already on the record.įirst, I started by writing a small c# console application to load a record, update the record and save the record back to cosmos. I would like to change the ‘schema’ of the data to support better reporting capability. I have a large collection of data stored in cosmos, ~100 million rows, but it fluctuates higher/lower over time.
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