diff --git a/wally/process_results.py b/wally/process_results.py
index 5ca53af..112826e 100644
--- a/wally/process_results.py
+++ b/wally/process_results.py
@@ -1,58 +1,53 @@
 # put all result preprocessing here
 # selection, aggregation
 
+from io import BytesIO
 import logging
-
+from typing import Any
 
 from .stage import Stage, StepOrder
 from .test_run_class import TestRun
 from .statistic import calc_norm_stat_props, calc_histo_stat_props
-from .result_classes import TestJobConfig
-from .suits.itest import ResultStorage
+from .result_classes import StatProps, DataSource, TimeSeries
+from .hlstorage import ResultStorage
 from .suits.io.fio_hist import get_lat_vals, expected_lat_bins
+from .suits.io.fio import FioTest
 from .utils import StopTestError
 
-logger = logging.getLogger("wally")
-
 import matplotlib
-
-# have to be before pyplot import to avoid tkinter(default graph frontend) import error
 matplotlib.use('svg')
-
 import matplotlib.pyplot as plt
 
 
+logger = logging.getLogger("wally")
+
+
 class CalcStatisticStage(Stage):
     priority = StepOrder.TEST + 1
 
     def run(self, ctx: TestRun) -> None:
-        rstorage = ResultStorage(ctx.storage, TestJobConfig)
+        rstorage = ResultStorage(ctx.storage)
 
-        for suite_cfg, path in rstorage.list_suites():
-            if suite_cfg.test_type != 'fio':
-                continue
-
-            for job_cfg, path, _ in rstorage.list_jobs_in_suite(path):
+        for suite in rstorage.iter_suite(FioTest.name):
+            for job in rstorage.iter_job(suite):
                 results = {}
-                for node_id, dev, sensor_name in rstorage.list_ts_in_job(path):
-                    ts = rstorage.load_ts(path, node_id, dev, sensor_name)
-                    if dev == 'fio' and sensor_name == 'lat':
+                for ts in rstorage.iter_ts(suite, job):
+                    if ts.source.sensor == 'lat':
                         if ts.second_axis_size != expected_lat_bins:
                             logger.error("Sensor %s.%s on node %s has" +
                                          "second_axis_size=%s. Can only process sensors with second_axis_size=%s.",
-                                         dev, sensor_name, node_id, ts.second_axis_size, expected_lat_bins)
+                                         ts.source.dev, ts.source.sensor, ts.source.node_id,
+                                         ts.second_axis_size, expected_lat_bins)
                             continue
                         ts.bins_edges = get_lat_vals(ts.second_axis_size)
-                        stat_prop = calc_histo_stat_props(ts)
+                        stat_prop = calc_histo_stat_props(ts)  # type: StatProps
 
                     elif ts.second_axis_size != 1:
                         logger.warning("Sensor %s.%s on node %s provide 2D data with " +
                                        "ts.second_axis_size=%s. Can't process it.",
-                                       dev, sensor_name, node_id, ts.second_axis_size)
+                                       ts.source.dev, ts.source.sensor, ts.source.node_id, ts.second_axis_size)
                         continue
                     else:
                         stat_prop = calc_norm_stat_props(ts)
 
-                    results[(node_id, dev, sensor_name)] = stat_prop
-
         raise StopTestError()