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  1. Core Server
  2. SERVER-20091

Poor query throughput and erratic behavior at high connection counts under WiredTiger

    • Fully Compatible
    • ALL
    • QuInt 8 08/28/15

      • single collection, 100k documents (fits in memory)
      • 12 cpus (6 cores)
      • workload is n connections each querying a random single document in a loop by _id
      • measured raw flat-out maximum capacity by using 150 connections each doing queries as fast as possible; similar numbers for WT (174k queries/s) and mmapv1 (204k queries/s)
      • then measured simulated customer app by introducing delay in loop so that each connection executes 10 queries/s, and then ramped number of connections up to 10k, for an expected throughput of 10 queries/connection/s * 10k connections = 100k queries/s. This is well below (about half) the measured maximum raw capacity for both WT and mmapv1, so expect to be able to achieve close to 100k queries/s at 10k connections
      • do achieve close to that for mmapv1 (75k queries/s), but only get about 25k queries/s for WT at 10k connections, and behavior is erratic

      mmapv1

      • max raw capacity is 204k queries/s (as described above, this is with 150 connections each issuing queries as fast as possible)
      • as connections are rampled up to 10k connections, this time with each connection issuing only 10 queries/s, throughput behavior is excellent up to about 6k connections, some mild falloff above that
      • at 10k connections getting about 75k queries/s (estimated by fitting the blue quadratic trendline), not too far below the expected 100k queries/s

      WiredTiger

      • max raw capacity is similar to mmapv1 at 174k queries/s (as described above, this is with 150 connections each issuing queries as fast as possible)
      • but as connections are rampled up to 10k connections, this time each connection issuing only 10 queries/s, above about 3k connections behavior becomes erratic
      • at 10k connections getting only about 25k queries/s (estimated by fitting the blue quadratic trendline), far below the expected 100k queries/s

      Repro code:

      function repro_setup() {
          x = []
          for (var i=0; i<100; i++)
              x.push(i)
          count = 100000
          every = 10000
          for (var i=0; i<count; ) {
              var bulk = db.c.initializeUnorderedBulkOp();
              for (var j=0; j<every; j++, i++)
                  bulk.insert({})
              bulk.execute();
              print(i)
          }
      }
      
      function conns() {
          return db.serverStatus().connections.current
      }
      
      function repro(threads_query) {
          start_conns = conns()
          while (conns() < start_conns+threads_query) {
              ops_query = [{
                  op: "query",
                  ns: "test.c",
                  query: {_id: {"#RAND_INT": [0, 10000]}},
                  delay: NumberInt(100 + Math.random()*10-5)
              }]
              res = benchStart({
                  ops: ops_query,
                  seconds: seconds,
                  parallel: 10,
              })
              sleep(100)
          }
      }
      

        1. combined.png
          combined.png
          23 kB
        2. mmapv1.png
          mmapv1.png
          19 kB
        3. mutex.png
          mutex.png
          18 kB
        4. mutex2.png
          mutex2.png
          15 kB
        5. NetworkCounters.patch
          2 kB
        6. network-mmap.png
          network-mmap.png
          19 kB
        7. network-wt.png
          network-wt.png
          20 kB
        8. slowms.png
          slowms.png
          21 kB
        9. variations.png
          variations.png
          23 kB
        10. wt.png
          wt.png
          22 kB

            Assignee:
            martin.bligh Martin Bligh
            Reporter:
            bruce.lucas@mongodb.com Bruce Lucas (Inactive)
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              Created:
              Updated:
              Resolved: