This repo is used for benchmarking Cosmos DB SDK against various workloads

The use-case is the following

• Persist the user provided sets (in practice this is a graph, but a set models the same workload in a simpler way) in the DB e.g. [ G1{A,B,C}, G2{X,Y}, G3{P,Q,R,S}]
• Support the query, provided any one of the set members return the original set. e.g. Given Q, return {P,Q,R,S}

To support this use case, we model the data using two collections

1. A graph collection that stores each Set with the key as the Set name (or GraphId). e.g. Key = "G1", Value = "A,B,C"
2. A routing collection that point each member to the Set name (or GraphId). e.g.for set G1, we have the following 3 documents - [Key = "A", Value = "G1"] [Key = "B", Value = "G1"] [Key = "C", Value = "G1"]

JMH is used as the benchmarking harness (https://openjdk.java.net/projects/code-tools/jmh/)
See com.adobe.platform.core.identity.services.cosmosdb.client.benchmark.jmh.ReadBenchmark for the JMH annotated class.

The following are the workloads that have been modelled

1. lookupRoutingSingle - Lookup a single document in the routing collection by calling readDocument(..)
2. lookupRoutingBatch - Lookup a batch of 1000 documents in the routing collection. This is done by grouping the 1000 keys by the partitionRangeId that they fall into and issuing 1 query per partition.
3. lookupTwoTableSingle - Given a key, do a lookup in Routing collection by calling readDocument(..), use the graph-id present in the retrieved document to fetch the corresponding graph from the Graph collection.
4. lookupTwoTableBatch - Same as #3, but for batches of 1000 keys. This translates into a call to lookupRoutingBatch, followed by a call to lookupGraphBatch.

• Provide correct CosmosDB Account/DB details. Update client-common/src/main/resources/reference.conf
• Configure the benchmark runner. Update benchmark/src/main/resources/reference.conf. Description of the config block follows

     jmh {
       params {
         jvmArgs = "-Xmx8G"
         jmhArgs = "-f1  -i 1 -r 1 -w 1 -wi 1 -t 1 -rf json" // see https://github.com/guozheng/jmh-tutorial/blob/master/README.md#jmh-command-line-options
         // -f 1    -> How many forks? Each fork is an independent benchmark on a separate JVM. Results are aggregated to provide mean and std-dev(error)
         // -i 1    -> Iterations share the same JVM. Results from each iteration are aggregated to provide mean and std-dev(error)
         // -r 1    -> How long does each iteration last? (default seconds).
         // -wi 1   -> How many warm-up iterations? These don't count towards the measurement.
         // -w 1    -> How long does each warm-up iteration last?
         // -t 1    -> How many concurrent threads to use for load generation? This is ignored, value from runList below is used instead.
         // -to 600 -> Timeout (seconds) for each iteration 
         resultsPath = "/tmp/"                              // The detailed results file for each run goes here
         summaryCsvFile = "/tmp/benchmark-results.csv"      // A simple consolidated summary of all the runs go here
       }
       runList = [                                          // Specify n number of benchmark runs
         {
           name = "lookup-single-v4"                        // Name for this run
           regex = "ReadBenchmark.lookupRoutingSingle"      // Regex to use to pickup benchmark methods
           threads = [1]//[1,50,100, 125]                   // Number of threads to use for benchmark. We do a separate benchmark for each thread in the array.
           clientType = "v4"                                // Specify which version of the SDK to use.
         }
       ]
     }
   }

• To build runnable jar run ./gradlew shadowJar
• Copy to target machine scp benchmark/build/libs/benchmark-1.2-cosmos-2.4.3-SNAPSHOT-shadow.jar [email protected]:~/
• Modify config after building shadow jar (optional)
  • You can modify the reference.conf file inside the runnable jar to configure DB account, DB name, MasterKey
  • vim benchmark-1.2-cosmos-2.4.3-SNAPSHOT-shadow.jar
  • Hit enter on reference.conf to enter file
  • Look for the following block and modify as needed. :wq to save file and :q! to exit Vim zip browser.

java -cp benchmark/build/libs/benchmark-1.2-cosmos-2.4.3-SNAPSHOT-shadow.jar com.adobe.platform.core.identity.services.datagenerator.main.DataGenUtil

java -cp benchmark/build/libs/benchmark-1.2-cosmos-2.4.3-SNAPSHOT-shadow.jar com.adobe.platform.core.identity.services.cosmosdb.client.benchmark.suite.BenchmarkSuiteRunner | tee benchmark.out
Note that the SuiteRunner runs in it's own separate JVM, the purpose of the SuiteRunner is the following

1. Execute all the benchmarks as per the spec in benchmark/src/main/resources/reference.conf
2. Spawn a JVM for each benchmark run (one-at-a-time) in #1 and aggregate the results into a single CSV file.

• Run this main method com.adobe.platform.core.identity.services.cosmosdb.client.benchmark.jmh.ReadBenchmark.main
• This will simply exercise readDocument(..)

1. Update benchmark/src/main/resources/reference.conf with jvmArgs = "-agentlib:jdwp=transport=dt_socket,server=y,suspend=y,address=5005" \
2. Following instructions in section Run benchmarks to start the suite runner. This will start the benchmark JVM (not the suite runner) in debug mode, listening on localhost:5005
3. Instructions to connect using IDEA Community Edition follows

• From the top menu-bar Run -> Edit Configuration -> + icon (top-left) to 'Add New Configuration' -> Select 'Remote' -> Rename your configuration to say 'JMH' -> Select port as 5005 -> Debugger mode = Attach to remote JVM -> Hit OK to save and close
• Set breakpoints as needed, say inside CosmosAsyncContainer#readItem(.)
• Select the newly created JMH run configuration from the drop down and hit the debug button. This will start the debug session.

Notes

• The default timeout for each JMH iteration is 10min. To increase, update jmhArgs in the config file