diff --git a/pipeline_dp/combiners.py b/pipeline_dp/combiners.py
index 83b1542b..d66b298e 100644
--- a/pipeline_dp/combiners.py
+++ b/pipeline_dp/combiners.py
@@ -1,4 +1,9 @@
 import abc
+import copy
+
+import pipeline_dp
+from pipeline_dp import dp_computations
+from pipeline_dp import budget_accounting
 
 
 class Combiner(abc.ABC):
@@ -9,6 +14,20 @@ class Combiner(abc.ABC):
     aggregation state. Combiners contain logic, while accumulators contain data.
     The API of combiners are inspired by Apache Beam CombineFn class.
     https://beam.apache.org/documentation/transforms/python/aggregation/combineperkey/#example-5-combining-with-a-combinefn
+
+    Here's how PipelineDP uses combiners to performs an aggregation on some
+    dataset X:
+    1. Split dataset X on sub-datasets which might be kept in memory.
+    2. Call create_accumulators() for each sub-dataset and keep resulting accumulators.
+    3. Choosing any pair of accumulators and merge them by calling merge_accumulators().
+    4. Continue 3 until 1 accumulator is left.
+    5. Call compute_metrics() for the accumulator that left.
+
+    Assumption: merge_accumulators is associative binary operation.
+
+    The type of accumulator depends on the aggregation performed by this Combiner.
+    For example, this can be a primitive type (e.g. int for a simple "count"
+    aggregation) or a more complex structure (e.g. for "mean")
     """
 
     @abc.abstractmethod
@@ -22,6 +41,59 @@ def merge_accumulators(self, accumulator1, accumulator2):
         pass
 
     @abc.abstractmethod
-    def compute_metrics(self, accumulator: 'Accumulator'):
+    def compute_metrics(self, accumulator):
         """Computes and returns the result of aggregation."""
         pass
+
+
+class CombinerParams:
+    """Parameters for a combiner.
+
+    Wraps all the information needed by the combiner to do the
+    differentially-private computation, e.g. privacy budget and mechanism.
+
+    Note: 'aggregate_params' is copied.
+    """
+
+    def __init__(self, spec: budget_accounting.MechanismSpec,
+                 aggregate_params: pipeline_dp.AggregateParams):
+        self._mechanism_spec = spec
+        self.aggregate_params = copy.copy(aggregate_params)
+
+    @property
+    def eps(self):
+        return self._mechanism_spec.eps
+
+    @property
+    def delta(self):
+        return self._mechanism_spec.delta
+
+    @property
+    def mean_var_params(self):
+        return dp_computations.MeanVarParams(
+            self.eps, self.delta, self.aggregate_params.low,
+            self.aggregate_params.high,
+            self.aggregate_params.max_partitions_contributed,
+            self.aggregate_params.max_contributions_per_partition,
+            self.aggregate_params.noise_kind)
+
+
+class CountCombiner(Combiner):
+    """Combiner for computing DP Count.
+
+    The type of the accumulator is int, which represents count of the elements
+    in the dataset for which this accumulator is computed.
+    """
+
+    def __init__(self, params: CombinerParams):
+        self._params = params
+
+    def create_accumulator(self, values) -> int:
+        return len(values)
+
+    def merge_accumulators(self, count1: int, count2: int):
+        return count1 + count2
+
+    def compute_metrics(self, count: int) -> float:
+        return dp_computations.compute_dp_count(count,
+                                                self._params.mean_var_params)
diff --git a/tests/combiners_test.py b/tests/combiners_test.py
index 1fc35ac7..5631b706 100644
--- a/tests/combiners_test.py
+++ b/tests/combiners_test.py
@@ -1,5 +1,66 @@
+import pipeline_dp
 import pipeline_dp.combiners as combiners
+import pipeline_dp.budget_accounting as ba
+
+import numpy as np
 import unittest
 
+
+def _create_mechism_spec(no_noise):
+    if no_noise:
+        eps, delta = 1e5, 1.0 - 1e-5
+    else:
+        eps, delta = 10, 1e-5
+
+    return ba.MechanismSpec(ba.MechanismType.GAUSSIAN, None, eps, delta)
+
+
+def _create_aggregate_params():
+    return pipeline_dp.AggregateParams(
+        low=0,
+        high=1,
+        max_partitions_contributed=1,
+        max_contributions_per_partition=3,
+        noise_kind=pipeline_dp.NoiseKind.GAUSSIAN,
+        metrics=[pipeline_dp.Metrics.COUNT])
+
+
+class CountAccumulatorTest(unittest.TestCase):
+
+    def _create_combiner(self, no_noise):
+        mechanism_spec = _create_mechism_spec(no_noise)
+        aggregate_params = _create_aggregate_params()
+        params = combiners.CombinerParams(mechanism_spec, aggregate_params)
+        return combiners.CountCombiner(params)
+
+    def test_create_accumulator(self):
+        for no_noise in [False, True]:
+            combiner = self._create_combiner(no_noise)
+            self.assertEqual(0, combiner.create_accumulator([]))
+            self.assertEqual(2, combiner.create_accumulator([1, 2]))
+
+    def test_merge_accumulators(self):
+        for no_noise in [False, True]:
+            combiner = self._create_combiner(no_noise)
+            self.assertEqual(0, combiner.merge_accumulators(0, 0))
+            self.assertEqual(5, combiner.merge_accumulators(1, 4))
+
+    def test_compute_metrics_no_noise(self):
+        combiner = self._create_combiner(no_noise=True)
+        self.assertAlmostEqual(3, combiner.compute_metrics(3), delta=1e-5)
+
+    def test_compute_metrics_with_noise(self):
+        combiner = self._create_combiner(no_noise=False)
+        accumulator = 5
+        noisified_values = [
+            combiner.compute_metrics(accumulator) for _ in range(1000)
+        ]
+        self.assertAlmostEqual(accumulator,
+                               np.mean(noisified_values),
+                               delta=1e-1)
+        self.assertTrue(
+            np.var(noisified_values) > 1)  # check that noise is added
+
+
 if __name__ == '__main__':
     unittest.main()