diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 2ddcb9d..744b8c6 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -17,3 +17,5 @@ jobs:
       - uses: olafurpg/setup-scala@v10
       - name: Test
         run: sbt -Dspark.testVersion=${{ matrix.spark  }} +test
+      - name: Benchmark
+        run: sbt -Dspark.testVersion=${{ matrix.spark  }} +benchmarks/Jmh/run
diff --git a/benchmarks/src/main/scala/com/github/mrpowers/spark/fast/tests/MyBenchmark.scala b/benchmarks/src/main/scala/com/github/mrpowers/spark/fast/tests/MyBenchmark.scala
index afacf9d..4da7450 100644
--- a/benchmarks/src/main/scala/com/github/mrpowers/spark/fast/tests/MyBenchmark.scala
+++ b/benchmarks/src/main/scala/com/github/mrpowers/spark/fast/tests/MyBenchmark.scala
@@ -1,59 +1,41 @@
 package com.github.mrpowers.spark.fast.tests
 
-import org.apache.spark.sql.Row
+import org.apache.spark.sql.SparkSession
 import org.openjdk.jmh.annotations._
 import org.openjdk.jmh.infra.Blackhole
 
-import java.time.Instant
 import java.util.concurrent.TimeUnit
+import scala.util.Try
 
-// TODO: move this to separate benchmark project
-private class MyBenchmark {
-
+private class MyBenchmark extends DataFrameComparer {
   @Benchmark
-  @BenchmarkMode(Array(Mode.AverageTime))
+  @BenchmarkMode(Array(Mode.AverageTime, Mode.SingleShotTime))
   @Fork(value = 2)
   @Warmup(iterations = 10)
   @Measurement(iterations = 10)
   @OutputTimeUnit(TimeUnit.NANOSECONDS)
-  def testMethod(blackHole: Blackhole): Boolean = {
-    val r1 = Row(
-      "a",
-      Row(
-        1,
-        Row(
-          2.0,
-          Row(
-            null,
-            Row(
-              Seq(Row("c"), Row("d")),
-              BigDecimal.decimal(1.0),
-              Row(Instant.EPOCH)
-            )
-          )
-        )
-      )
-    )
+  def assertApproximateDataFrameEqualityWithPrecision(blackHole: Blackhole): Boolean = {
+    val spark = SparkSession
+      .builder()
+      .master("local")
+      .appName("spark session")
+      .config("spark.sql.shuffle.partitions", "1")
+      .getOrCreate()
+    spark.sparkContext.setLogLevel("ERROR")
+
+    import spark.implicits._
+    val ds1 = Seq(
+      ("1", "10/01/2019", 26.762499999999996),
+      ("1", "11/01/2019", 26.762499999999996)
+    ).toDF("col_B", "col_C", "col_A")
+
+    val ds2 = Seq(
+      ("1", "10/01/2019", 26.762499999999946),
+      ("1", "11/01/2019", 26.76249999999991)
+    ).toDF("col_B", "col_C", "col_A")
+    val result = Try(assertApproximateDataFrameEquality(ds1, ds2, precision = 0.0000001, orderedComparison = false))
 
-    val r2 = Row(
-      "a",
-      Row(
-        1,
-        Row(
-          2.0,
-          Row(
-            null,
-            Row(
-              Seq(Row("c"), Row("d")),
-              BigDecimal.decimal(1.0),
-              Row(Instant.EPOCH)
-            )
-          )
-        )
-      )
-    )
-    val bool = RowComparer.areRowsEqual(r1, r2)
-    blackHole.consume(bool)
-    bool
+    blackHole.consume(result)
+    result.isSuccess
   }
 }