![]() If your benchmark does not include range queries, you can use hash-based sharding to ensure a uniform distribution of writes and reads. By pre-splitting the data, documents will be loaded in parallel into the appropriate shards. Without pre-splitting, data may be loaded into a shard then moved to a different shard as the load progresses. When creating a new sharded collection, pre-split chunks before loading. Similarly, to reduce the overhead from network round trips, you can use bulk writes to load (or update) many documents in one batch. Use Multiple Parallel ThreadsĮspecially for a sharded cluster, and certain configurations like writeConcern majority, latencies for a single operation can be significant, and using multiple threads is necessary to drive good throughput. The following considerations will help you develop benchmarks that are meaningful. We instead recommend that you model your benchmark using the data, queries, and deployment environment that are representative of your application. Generic benchmarks can be misleading and mis-representative of any technology and how well it will perform for a given application. Data modeling and sizing memory (the working set).In this series, we have covered considerations for achieving performance at scale across a number of important dimensions, including: Initially, the status of the job is Started and it includes a timestamp in the JobStartTime property but not the JobFinishTime property.Welcome to the final part in our series of blog posts covering performance best practices for MongoDB. The results should return a single job with JobStartTime, JobStatus, and JobFinishTime properties. connection-string $storageConnectionString \ Query the job record in a storage table named ycsbbenchmarkingmetadata using az storage entity query. Operation,Count,Throughput,Min(microsecond),Max(microsecond),Avg(microsecond),P9S(microsecond),P99(microsecond) You should now have a CSV dataset with aggregated results from all the benchmark clients. Open the aggregation.csv blob and observe the content. Observe the output and diagnostic blobs for the tool. ![]() Navigate to the storage container in the same account with a prefix of ycsbbenchmarking-*. It can take approximately 20-30 minutes for the job to finish. Now, you use an Azure Resource Manager template to deploy the benchmarking framework to Azure with the default read recipe. Using the az cosmosdb sql container create command, create a new container with the following settings: Setting resource-group $sourceResourceGroupName \ Using the az cosmosdb sql database create command, create a new database with the following settings: Setting # Variable for storage account connection string # Variable for Azure Storage account name # Variable for API for NoSQL endpoint URI # Variable for resource group with Azure Cosmos DB and Azure Storage accounts # Variable for Azure Cosmos DB for NoSQL account name
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