diff --git a/analytics/analytic_types/data_bucket.py b/analytics/analytic_types/data_bucket.py
index 5eb3809..c1a91df 100755
--- a/analytics/analytic_types/data_bucket.py
+++ b/analytics/analytic_types/data_bucket.py
@@ -1,14 +1,33 @@
+from typing import Optional
+
 import pandas as pd
 
 
 class DataBucket:
 
-    def __init__(self):
+    def __init__(self, max_size: Optional[int] = None):
+        self.set_max_size(max_size)
         self.data = pd.DataFrame([], columns=['timestamp', 'value'])
 
-    def receive_data(self, data: pd.DataFrame):
+    def append_data(self, data: pd.DataFrame) -> None:
         self.data = self.data.append(data, ignore_index=True)
 
-    def drop_data(self, count: int):
+        if self.max_size == None:
+            return
+
+        bucket_size = self.get_current_size()
+        if bucket_size > self.max_size:
+            extra_data_count = bucket_size - self.max_size
+            self.drop_data(extra_data_count)
+
+    def drop_data(self, count: int) -> None:
         if count > 0:
             self.data = self.data.iloc[count:]
+
+    def set_max_size(self, max_size: Optional[int] = None) -> None:
+        if max_size is not None and max_size < 0:
+            raise Exception(f'Can`t set negative max size for bucket: {max_size}')
+        self.max_size = max_size
+
+    def get_current_size(self) -> int:
+        return len(self.data)
diff --git a/analytics/detectors/anomaly_detector.py b/analytics/detectors/anomaly_detector.py
index 7885d01..870bde4 100644
--- a/analytics/detectors/anomaly_detector.py
+++ b/analytics/detectors/anomaly_detector.py
@@ -17,6 +17,9 @@ import utils
 MAX_DEPENDENCY_LEVEL = 100
 MIN_DEPENDENCY_FACTOR = 0.1
 BASIC_ALPHA = 0.5
+
+BUCKET_SIZE = MAX_DEPENDENCY_LEVEL
+
 logger = logging.getLogger('ANOMALY_DETECTOR')
 
 
@@ -105,9 +108,12 @@ class AnomalyDetector(ProcessingDetector):
         if len(data_without_nan) == 0:
             return None
 
-        self.bucket.receive_data(data_without_nan)
+        window_size = self.get_window_size(cache)
+
+        self.bucket.set_max_size(BUCKET_SIZE)
+        self.bucket.append_data(data_without_nan)
 
-        if len(self.bucket.data) >= self.get_window_size(cache):
+        if self.bucket.get_current_size() >= window_size:
             return self.detect(self.bucket.data, cache)
 
         return None
@@ -264,7 +270,8 @@ class AnomalyDetector(ProcessingDetector):
                 yield Segment(
                     utils.convert_pd_timestamp_to_ms(segment_start),
                     utils.convert_pd_timestamp_to_ms(segment_end),
-                    message=f'{val} out of {str(bound.value)} bound'
+                    # TODO: configurable decimals number
+                    message=f'{val:.2f} out of {str(bound.value)} bound'
                 )
                 in_segment = False
         else:
@@ -273,5 +280,5 @@ class AnomalyDetector(ProcessingDetector):
                 return Segment(
                     utils.convert_pd_timestamp_to_ms(segment_start),
                     utils.convert_pd_timestamp_to_ms(segment_end),
-                    message=f'{val} out of {str(bound.value)} bound'
+                    message=f'{val:.2f} out of {str(bound.value)} bound'
                 )
diff --git a/analytics/detectors/pattern_detector.py b/analytics/detectors/pattern_detector.py
index 3e3a949..4b1a7c4 100644
--- a/analytics/detectors/pattern_detector.py
+++ b/analytics/detectors/pattern_detector.py
@@ -110,23 +110,20 @@ class PatternDetector(Detector):
         if len(data_without_nan) == 0:
             return None
 
-        self.bucket.receive_data(data_without_nan)
-
         # TODO: use ModelState
         window_size = cache['windowSize']
+        bucket_max_size = max(window_size * self.BUCKET_WINDOW_SIZE_FACTOR, self.MIN_BUCKET_SIZE)
+
+        self.bucket.set_max_size(bucket_max_size)
+        self.bucket.append_data(data_without_nan)
 
-        bucket_len = len(self.bucket.data)
-        if bucket_len < window_size * 2:
-            msg = f'{self.analytic_unit_id} bucket data {bucket_len} less than two window size {window_size * 2}, skip run detection from consume_data'
+        bucket_size = self.bucket.get_current_size()
+        if bucket_size < window_size * 2:
+            msg = f'{self.analytic_unit_id} bucket data {bucket_size} less than two window size {window_size * 2}, skip run detection from consume_data'
             logger.debug(msg)
             return None
 
-        res = self.detect(self.bucket.data, cache)
-
-        bucket_size = max(window_size * self.BUCKET_WINDOW_SIZE_FACTOR, self.MIN_BUCKET_SIZE)
-        if bucket_len > bucket_size:
-            excess_data = bucket_len - bucket_size
-            self.bucket.drop_data(excess_data)
+        res = self.detect(self.bucket.data, cache)        
 
         logging.debug('End consume_data for analytic unit: {} with res: {}'.format(self.analytic_unit_id, str(res.to_json())))
 
diff --git a/tests/test_bucket.py b/tests/test_bucket.py
index 8bd138c..203a1d2 100644
--- a/tests/test_bucket.py
+++ b/tests/test_bucket.py
@@ -13,17 +13,17 @@ class TestBucket(unittest.TestCase):
         data_val = list(range(6))
         timestamp_list = create_list_of_timestamps(len(data_val))
         for val in data_val:
-            bucket.receive_data(get_pd_dataframe([val], [1523889000000 + val]))
+            bucket.append_data(get_pd_dataframe([val], [1523889000000 + val]))
         for idx, row in bucket.data.iterrows():
             self.assertEqual(data_val[idx], row['value'])
             self.assertEqual(timestamp_list[idx], row['timestamp'])
 
-    def test_drop_data(self):
+    def test_max_size(self):
         bucket = DataBucket()
         data_val = list(range(10))
         timestamp_list = create_list_of_timestamps(len(data_val))
-        bucket.receive_data(get_pd_dataframe(data_val, timestamp_list))
-        bucket.drop_data(5)
+        bucket.set_max_size(5)
+        bucket.append_data(get_pd_dataframe(data_val, timestamp_list))
         expected_data = data_val[5:]
         expected_timestamp = timestamp_list[5:]
         self.assertEqual(expected_data, bucket.data['value'].tolist())