Repository: stephanecollot/sparkmon (language: python)

Issue:
fixing stackoverflow and race condition issues



Discussion / hints:

Fix patch:
diff --git a/.cookiecutter.json b/.cookiecutter.json
index 3153e58..4390667 100644
--- a/.cookiecutter.json
+++ b/.cookiecutter.json
@@ -4,7 +4,7 @@
   "email": "stephane.collot@ing.com",
   "friendly_name": "sparkmon",
   "github_user": "stephanecollot",
-  "license": "Apache-2.0",
+  "license": "MIT",
   "package_name": "sparkmon",
   "project_name": "sparkmon",
   "version": "0.0.0"
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index c332589..0a62af2 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -2,7 +2,7 @@ Contributor Guide
 =================
 
 Thank you for your interest in improving this project.
-This project is open-source under the `Apache 2.0 license`_ and
+This project is open-source under the `MIT license`_ and
 welcomes contributions in the form of bug reports, feature requests, and pull requests.
 
 Here is a list of important resources for contributors:
@@ -12,7 +12,7 @@ Here is a list of important resources for contributors:
 - `Issue Tracker`_
 - `Code of Conduct`_
 
-.. _Apache 2.0 license: https://opensource.org/licenses/Apache-2.0
+.. _MIT license: https://opensource.org/licenses/MIT
 .. _Source Code: https://github.com/stephanecollot/sparkmon
 .. _Documentation: https://sparkmon.readthedocs.io/
 .. _Issue Tracker: https://github.com/stephanecollot/sparkmon/issues
diff --git a/LICENSE.rst b/LICENSE.rst
index 1fb8c07..0dc3bbd 100644
--- a/LICENSE.rst
+++ b/LICENSE.rst
@@ -1,219 +1,22 @@
-Copyright 2021 Stephane Collot
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-
-------------------------------------------------------------------------
-
-Apache License
-==============
-
-Version 2.0, January 2004
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-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+MIT License
+===========
+
+Copyright © 2021 ING Wholesale Banking Advanced Analytics
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+**The software is provided "as is", without warranty of any kind, express or
+implied, including but not limited to the warranties of merchantability,
+fitness for a particular purpose and noninfringement. In no event shall the
+authors or copyright holders be liable for any claim, damages or other
+liability, whether in an action of contract, tort or otherwise, arising from,
+out of or in connection with the software or the use or other dealings in the
+software.**
diff --git a/README.rst b/README.rst
index 5402dfa..371a0d6 100644
--- a/README.rst
+++ b/README.rst
@@ -14,7 +14,7 @@ sparkmon
    :target: https://pypi.org/project/sparkmon
    :alt: Python Version
 .. |License| image:: https://img.shields.io/pypi/l/sparkmon
-   :target: https://opensource.org/licenses/Apache-2.0
+   :target: https://opensource.org/licenses/MIT
    :alt: License
 .. |Read the Docs| image:: https://img.shields.io/readthedocs/sparkmon/latest.svg?label=Read%20the%20Docs
    :target: https://sparkmon.readthedocs.io/
@@ -117,7 +117,7 @@ To learn more, see the `Contributor Guide`_.
 License
 -------
 
-Distributed under the terms of the `Apache 2.0 license`_,
+Distributed under the terms of the `MIT license`_,
 *sparkmon* is free and open source software.
 
 
@@ -135,7 +135,7 @@ This project was generated from `@cjolowicz`_'s `Hypermodern Python Cookiecutter
 
 .. _@cjolowicz: https://github.com/cjolowicz
 .. _Cookiecutter: https://github.com/audreyr/cookiecutter
-.. _Apache 2.0 license: https://opensource.org/licenses/Apache-2.0
+.. _MIT license: https://opensource.org/licenses/MIT
 .. _PyPI: https://pypi.org/
 .. _Hypermodern Python Cookiecutter: https://github.com/cjolowicz/cookiecutter-hypermodern-python
 .. _file an issue: https://github.com/stephanecollot/sparkmon/issues
diff --git a/codecov.yml b/codecov.yml
index 39c15fc..69160ea 100644
--- a/codecov.yml
+++ b/codecov.yml
@@ -6,4 +6,4 @@ coverage:
         target: "90"
     patch:
       default:
-        target: "90"
+        target: "50"
diff --git a/docs/example.ipynb b/docs/example.ipynb
index 4b0417b..f75bf38 100644
--- a/docs/example.ipynb
+++ b/docs/example.ipynb
@@ -14,8 +14,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:22:48.827216Z",
-     "start_time": "2021-07-07T19:22:48.247655Z"
+     "end_time": "2021-07-21T20:46:16.017137Z",
+     "start_time": "2021-07-21T20:46:15.446908Z"
     },
     "lines_to_next_cell": 0
    },
@@ -31,8 +31,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:22:49.396031Z",
-     "start_time": "2021-07-07T19:22:48.830654Z"
+     "end_time": "2021-07-21T20:46:16.621223Z",
+     "start_time": "2021-07-21T20:46:16.020133Z"
     },
     "lines_to_next_cell": 0
    },
@@ -46,8 +46,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:22:59.350764Z",
-     "start_time": "2021-07-07T19:22:49.399844Z"
+     "end_time": "2021-07-21T20:46:25.688226Z",
+     "start_time": "2021-07-21T20:46:16.624398Z"
     }
    },
    "outputs": [],
@@ -72,8 +72,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:22:59.464219Z",
-     "start_time": "2021-07-07T19:22:59.354839Z"
+     "end_time": "2021-07-21T20:46:25.784888Z",
+     "start_time": "2021-07-21T20:46:25.692219Z"
     }
    },
    "outputs": [],
@@ -87,8 +87,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:22:59.504034Z",
-     "start_time": "2021-07-07T19:22:59.466855Z"
+     "end_time": "2021-07-21T20:46:25.815516Z",
+     "start_time": "2021-07-21T20:46:25.787210Z"
     }
    },
    "outputs": [],
@@ -99,17 +99,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:23:06.477308Z",
-     "start_time": "2021-07-07T19:22:59.506940Z"
+     "end_time": "2021-07-21T20:46:52.602128Z",
+     "start_time": "2021-07-21T20:46:43.331880Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2016x1152 with 10 Axes>"
       ]
@@ -142,13 +142,14 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:23:06.534945Z",
-     "start_time": "2021-07-07T19:23:06.479431Z"
+     "end_time": "2021-07-21T20:46:32.361525Z",
+     "start_time": "2021-07-21T20:46:32.335764Z"
     }
    },
    "outputs": [],
    "source": [
     "# Re trigger parsing for development purposes\n",
+    "app.debug = True\n",
     "app.parse_db()"
    ]
   },
@@ -157,14 +158,14 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:23:08.265750Z",
-     "start_time": "2021-07-07T19:23:06.540398Z"
+     "end_time": "2021-07-21T20:46:33.924673Z",
+     "start_time": "2021-07-21T20:46:32.365344Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 2016x1152 with 10 Axes>"
       ]
@@ -181,16 +182,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:23:26.876197Z",
-     "start_time": "2021-07-07T19:23:26.823941Z"
+     "end_time": "2021-07-21T20:46:33.953039Z",
+     "start_time": "2021-07-21T20:46:33.927319Z"
     }
    },
    "outputs": [],
    "source": [
-    "# mon.application.executors_db"
+    "# mon.application.timeseries_db"
    ]
   },
   {
@@ -198,8 +199,8 @@
    "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:23:08.478895Z",
-     "start_time": "2021-07-07T19:23:08.413811Z"
+     "end_time": "2021-07-21T20:46:33.980559Z",
+     "start_time": "2021-07-21T20:46:33.955931Z"
     }
    },
    "outputs": [],
@@ -212,8 +213,8 @@
    "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-07-07T19:23:08.549127Z",
-     "start_time": "2021-07-07T19:23:08.484926Z"
+     "end_time": "2021-07-21T20:46:34.008642Z",
+     "start_time": "2021-07-21T20:46:33.982755Z"
     }
    },
    "outputs": [],
diff --git a/pyproject.toml b/pyproject.toml
index 8c5125d..869408a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ name = "sparkmon"
 version = "0.0.3"
 description = "sparkmon"
 authors = ["Stephane Collot <stephane.collot@ing.com>"]
-license = "Apache-2.0"
+license = "MIT"
 readme = "README.rst"
 homepage = "https://github.com/stephanecollot/sparkmon"
 repository = "https://github.com/stephanecollot/sparkmon"
diff --git a/src/sparkmon/application.py b/src/sparkmon/application.py
index d48b77a..3021353 100644
--- a/src/sparkmon/application.py
+++ b/src/sparkmon/application.py
@@ -1,4 +1,23 @@
+# Copyright (c) 2021 ING Wholesale Banking Advanced Analytics
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 """Spark communication interface with its API, and managing historical API calls."""
+import warnings
 from datetime import datetime
 from typing import Any
 from typing import Dict
@@ -11,7 +30,8 @@
 
 import sparkmon
 from sparkmon.utils import flatten_dict
-from sparkmon.utils import get_memory
+from sparkmon.utils import get_memory_process
+from sparkmon.utils import get_memory_user
 
 API_APPLICATIONS_LINK = "api/v1/applications"
 WEB_URL = "http://localhost:4040"
@@ -20,16 +40,19 @@
 class Application:
     """This class is an helper to query Spark API and save historical."""
 
-    def __init__(self, application_id: str, web_url: str = WEB_URL) -> None:
+    def __init__(self, application_id: str, web_url: str = WEB_URL, debug: bool = False) -> None:
         """An application is define by the Spark UI link and an application id.
 
         :param application_id: Spark applicationId
         :param web_url: Spark REST API server
+        :param debug: debug mode for development purposes, it store the row data
         """
         self.web_url = web_url
         self.application_id = application_id
+        self.debug = debug
 
         self.executors_db: Dict[Any, Any] = {}  # The key is timestamp
+        self.timeseries_db: Dict[Any, Any] = {}  # The key is timestamp
         self.stages_df: pd.DataFrame = pd.DataFrame()
         self.tasks_db: Dict[str, Dict[Any, Any]] = {}  # The key is stageId.attemptId
 
@@ -50,22 +73,25 @@ def log_executors_info(self) -> None:
         executors_df = self.get_executors_info()
 
         now = pd.to_datetime(datetime.now())
-        self.executors_db[now] = {
-            "executors_df": executors_df,  # Storing full row data
-            "local_memory_pct": psutil.virtual_memory()[2],  # Local machine memory usage
-            "process_memory_usage": get_memory(),  # Python process memory usage in bytes
-        }
-        self.executors_db[now].update(self.parse_executors(executors_df))
+        if self.debug:
+            self.executors_db[now] = executors_df  # Storing full row data
+
+        self.timeseries_db[now] = self.parse_executors(executors_df)
+        # Remark: local_memory_pct is a percentage, doesn't really work if you are running an kube
+        self.timeseries_db[now]["local_memory_pct"] = psutil.virtual_memory()[2]  # Local machine memory usage
+        self.timeseries_db[now]["process_memory_usage"] = get_memory_process()  # Local machine memory usage
+        self.timeseries_db[now]["user_memory_usage"] = get_memory_user()  # Local machine memory usage
 
     def parse_db(self) -> None:
         """Re-parse the full executors_db, usefull if you change the parsing function, for development."""
-        for t, v in self.executors_db.items():
-            executors_df = v["executors_df"]
-            self.executors_db[t].update(self.parse_executors(executors_df))
+        if not self.debug:
+            print("sparkmon: Warning, parse_db should be used with debug=True")
+        for t, executors_df in self.executors_db.items():
+            self.timeseries_db[t].update(self.parse_executors(executors_df))
 
     def plot(self) -> None:
         """Plotting."""
-        sparkmon.plot_db(self.executors_db)
+        sparkmon.plot_timeseries(self.get_timeseries_db_df(), title=self.application_id)
 
     @staticmethod
     def parse_executors(executors_df: pd.DataFrame) -> Dict[Any, Any]:
@@ -93,28 +119,35 @@ def parse_executors(executors_df: pd.DataFrame) -> Dict[Any, Any]:
         )
         executors_df["memoryUsedPct"] = executors_df["memoryUsed"] / executors_df["maxMemory"] * 100
 
-        def mmm(d: Dict[str, Any], executors_df: pd.DataFrame, col: str) -> Dict[str, Any]:
-            if col not in executors_df.columns:
-                return d
-            d[f"{col}_max"] = executors_df[col].max()
-            d[f"{col}_mean"] = executors_df[col].mean()
-            d[f"{col}_min"] = executors_df[col].min()
-            d[f"{col}_median"] = executors_df[col].median()
-
-            return d
-
-        d: Dict[str, Any] = {}
-        d = mmm(d, executors_df, "memoryUsedPct")
-        d = mmm(d, executors_df, "usedOnHeapStorageMemoryPct")
-        d = mmm(d, executors_df, "usedOffHeapStorageMemoryPct")
-        d = mmm(d, executors_df, "totalOnHeapStorageMemory")
-        d = mmm(d, executors_df, "totalOffHeapStorageMemory")
-        d = mmm(d, executors_df, "ProcessTreePythonVMemory")
-        d = mmm(d, executors_df, "ProcessTreePythonRSSMemory")
-        d = mmm(d, executors_df, "JVMHeapMemory")
-        d = mmm(d, executors_df, "JVMOffHeapMemory")
-        d = mmm(d, executors_df, "OffHeapExecutionMemory")
-        d = mmm(d, executors_df, "OnHeapExecutionMemory")
+        # Columns to aggregate
+        cols = [
+            "memoryUsedPct",
+            "usedOnHeapStorageMemoryPct",
+            "usedOffHeapStorageMemoryPct",
+            "totalOnHeapStorageMemory",
+            "totalOffHeapStorageMemory",
+            "ProcessTreePythonVMemory",
+            "ProcessTreePythonRSSMemory",
+            "JVMHeapMemory",
+            "JVMOffHeapMemory",
+            "OffHeapExecutionMemory",
+            "OnHeapExecutionMemory",
+        ]
+
+        # Filter columns that are not on the DataFrame
+        cols = [col for col in cols if col in executors_df.columns]
+
+        # Obtain aggregated values
+        # Let's filter: "RuntimeWarning: Mean of empty slice"
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=RuntimeWarning)
+            res = executors_df[cols].agg(["max", "mean", "median", "min"])
+
+        # Convert to dict
+        d: Dict[str, Any] = {
+            f"{key}_{agg}": value for key, values in res.to_dict().items() for agg, value in values.items()
+        }
+
         d["numActive"] = len(executors_df.query("isActive"))
         d["memoryUsed_sum"] = executors_df["memoryUsed"].sum()
         d["maxMemory_sum"] = executors_df["maxMemory"].sum()
@@ -174,6 +207,11 @@ def get_tasks_df(self) -> pd.DataFrame:
         tasks_df = pd.DataFrame(tasks_list)
         return tasks_df
 
+    def get_timeseries_db_df(self) -> pd.DataFrame:
+        """Return timeseries_db info into a DataFrame."""
+        timeseries_db_df = pd.DataFrame(self.timeseries_db).T
+        return timeseries_db_df
+
     def log_all(self) -> None:
         """Updating all information."""
         self.log_executors_info()
diff --git a/src/sparkmon/callbacks.py b/src/sparkmon/callbacks.py
index 0944fb1..dacd4a2 100644
--- a/src/sparkmon/callbacks.py
+++ b/src/sparkmon/callbacks.py
@@ -1,45 +1,78 @@
+# Copyright (c) 2021 ING Wholesale Banking Advanced Analytics
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 """List of default callbacks."""
 from pathlib import Path
 
 import matplotlib
 import matplotlib.pyplot as plt
-import pandas as pd
 
 from sparkmon import Application
 from sparkmon.mlflow_utils import log_file
 
 
 def plot_to_image(application: Application, path: str = "sparkmon.png") -> None:
-    """Plot and save to image."""
+    """Plot and save to image.
+
+    Not compatible with live_plot_notebook().
+    """
     path = Path(path)
     path.parent.mkdir(parents=True, exist_ok=True)
 
     # To avoid the following error: "RuntimeError: main thread is not in main loop"
-    old_backend = matplotlib.get_backend()
-    plt.switch_backend("agg")
+    backend = "agg"
+    if matplotlib.get_backend() != backend:
+        print("set backend")
+        plt.switch_backend(backend)
 
     application.plot()
     plt.savefig(path)
-    plt.switch_backend(old_backend)
+
+    # To avoid memory leak
+    plt.clf()
+    plt.close()
 
 
 def plot_to_mlflow(application: Application, path: str = "sparkmon/plot.png") -> None:
-    """Log image to mlflow."""
+    """Log image to mlflow.
+
+    Not compatible with live_plot_notebook().
+    """
     # To avoid the following error: "RuntimeError: main thread is not in main loop"
-    old_backend = matplotlib.get_backend()
-    plt.switch_backend("agg")
+    backend = "agg"
+    if matplotlib.get_backend() != backend:
+        print("set backend")
+        plt.switch_backend(backend)
 
     application.plot()
     with log_file(path) as fp:
         plt.savefig(fp.name)
-    plt.switch_backend(old_backend)
+
+    # To avoid memory leak
+    plt.clf()
+    plt.close()
 
 
-def log_executors_db_to_mlflow(application: Application, path: str = "sparkmon/executors_db.csv") -> None:
-    """Log executors_db to mlflow."""
-    executors_db_df = pd.DataFrame(application.executors_db).T
+def log_timeseries_db_to_mlflow(application: Application, path: str = "sparkmon/timeseries.csv") -> None:
+    """Log timeseries_db to mlflow."""
+    timeseries_db_df = application.get_timeseries_db_df()
     with log_file(path) as fp:
-        executors_db_df.to_csv(fp, index=False)
+        timeseries_db_df.to_csv(fp, index=False)
 
 
 def log_tasks_to_mlflow(application: Application, path: str = "sparkmon/tasks.csv") -> None:
@@ -55,8 +88,8 @@ def log_stages_to_mlflow(application: Application, path: str = "sparkmon/stages.
 
 
 def log_to_mlflow(application: Application) -> None:
-    """Log executors_db to mlflow."""
+    """Log to mlflow."""
     plot_to_mlflow(application)
-    log_executors_db_to_mlflow(application)
+    log_timeseries_db_to_mlflow(application)
     log_tasks_to_mlflow(application)
     log_stages_to_mlflow(application)
diff --git a/src/sparkmon/mlflow_utils.py b/src/sparkmon/mlflow_utils.py
index 22c5271..f35f428 100644
--- a/src/sparkmon/mlflow_utils.py
+++ b/src/sparkmon/mlflow_utils.py
@@ -1,3 +1,21 @@
+# Copyright (c) 2021 ING Wholesale Banking Advanced Analytics
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 """mlflow missing utilities."""
 import tempfile
 from contextlib import contextmanager
@@ -6,7 +24,7 @@
 
 @contextmanager
 def log_file(artifact_full_path: str):
-    """Yield a file-object that is going to be savec as an artifact in mlflow.
+    """Yield a file-object that is going to be saved as an artifact in mlflow.
 
     mlflow API is really missing this functionality, so let's implement it via a temporary directory.
     """
diff --git a/src/sparkmon/monitor.py b/src/sparkmon/monitor.py
index b821866..27b383d 100644
--- a/src/sparkmon/monitor.py
+++ b/src/sparkmon/monitor.py
@@ -1,5 +1,22 @@
+# Copyright (c) 2021 ING Wholesale Banking Advanced Analytics
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 """Monitor thread."""
-import concurrent.futures
 import threading
 import time
 import urllib
@@ -16,10 +33,16 @@ class SparkMon(threading.Thread):
 
     This is a daemon thread, meaning that it is not blocking the exit of the application,
     and can be abruptly stopped at shutdown.
-    But it is running the callbacks into a ThreadPool that is blocking,
+    But it is running the callbacks into a different Thread that is blocking,
     meaning that they need to terminate to let the application exit.
     Indeed, it is important to not run the callbacks in the daemon,
     so that a file export callback wouldn't be interupted in the middle of saving.
+
+    In case of problem, we could do another architecture: just one thread, this one,
+    with deamon=False, and using 'atexit' to safely end the monitoring and not blocking the app.
+
+    Remark: The same 'application' should not be updated by something else, like other SparkMon instances,
+    because it could create race conditions.
     """
 
     def __init__(
@@ -34,13 +57,13 @@ def __init__(
         self.daemon = True
         self.cnt = 0
         self.application = application
+        self.application_lock = threading.Lock()
         self.period = period
         if callbacks is None:
             callbacks = []
         self.callbacks = callbacks
         self.updateEvent = threading.Event()
-        self.callbacks_pool = concurrent.futures.ThreadPoolExecutor()
-        self.callbacks_future = None
+        self.callbacks_thread = None
 
     def stop(self) -> None:
         """To stop the thread."""
@@ -59,8 +82,10 @@ def run(self) -> None:
             ###
             # Updating the application DB at the regular period:
             try:
-                self.application.log_all()
-                self.cnt += 1
+                # Callbacks are reading application, so let's make thread safe with a lock:
+                with self.application_lock:
+                    self.application.log_all()
+                    self.cnt += 1
             except urllib.error.URLError as ex:
                 if self.cnt > 1:
                     print(f"sparkmon: Spark application not available anymore. Exception: {ex}")
@@ -71,12 +96,13 @@ def run(self) -> None:
             # Callback can be run at a slower pace, specially if they are slow/expensive:
 
             # Check if callbacks are still running
-            if self.callbacks_future is not None and not self.callbacks_future.running():
-                self.callbacks_future = None
+            if self.callbacks_thread is not None and not self.callbacks_thread.is_alive():
+                self.callbacks_thread = None
 
             # We only run the callbacks if they are not running
-            if self.callbacks_future is None:
-                self.callbacks_future = self.callbacks_pool.submit(self.callbacks_run)
+            if self.callbacks_thread is None:
+                self.callbacks_thread = threading.Thread(target=self.callbacks_run)
+                self.callbacks_thread.start()
 
             self.updateEvent.set()
             time.sleep(self.period)
@@ -84,10 +110,20 @@ def run(self) -> None:
     def callbacks_run(self):
         """Running the callbacks."""
         for callback in self.callbacks:
-            callback(self.application)
+            with self.application_lock:
+                callback(self.application)
 
     def live_plot_notebook(self, n_iter=None) -> None:
-        """Useful in the remote case only."""
+        """Useful in the remote case only.
+
+        This is not compatible with callbacks that are using matplotlib, because matplotlib is not thread safe,
+        and you can get the following errors:
+        ```
+        python(81469,0x1106c5e00) malloc: Incorrect checksum for freed object 0x7fe18da140a8: probably modified after being freed.
+        Corrupt value: 0x230017000b00f005
+        python(81469,0x1106c5e00) malloc: *** set a breakpoint in malloc_error_break to debug
+        ```
+        """
         cnt = 0
         while True:
             cnt += 1
@@ -96,7 +132,16 @@ def live_plot_notebook(self, n_iter=None) -> None:
             if self.stopped():
                 return
 
-            sparkmon.plot_notebook(self.application)
-
+            with self.application_lock:
+                sparkmon.plot_notebook(self.application)
             self.updateEvent.clear()
             self.updateEvent.wait()
+
+    def __enter__(self):
+        """Start thread in contextmanager."""
+        self.start()
+        return self
+
+    def __exit__(self, *args, **kwargs):
+        """Stop thread in contextmanager."""
+        self.stop()
diff --git a/src/sparkmon/plotting.py b/src/sparkmon/plotting.py
index 3e1c8bc..ab05541 100644
--- a/src/sparkmon/plotting.py
+++ b/src/sparkmon/plotting.py
@@ -1,7 +1,24 @@
+# Copyright (c) 2021 ING Wholesale Banking Advanced Analytics
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 """Plotting utilities."""
 from typing import Any
 from typing import Callable
-from typing import Dict
 
 import matplotlib
 import matplotlib.pyplot as plt
@@ -23,91 +40,124 @@ def plot_max_value(ax: matplotlib.axes.Axes, s: Any, string_rep: Callable = lamb
     )
 
 
+def ax_convert_size(ax: matplotlib.axes.Axes):
+    """Convert byte size for displaying tick values."""
+    ticks = [convert_size(x) for x in ax.get_yticks()]
+    ax.set_yticks(
+        ax.get_yticks()
+    )  # Necessary to remove UserWarning: FixedFormatter should only be used together with FixedLocator
+    ax.set_yticklabels(ticks)
+
+
 def mmm_plot(
     ax: matplotlib.axes.Axes,
     value: str,
-    executors_db_df: pd.DataFrame,
+    timeseries_db_df: pd.DataFrame,
     title_prefix: str,
     pct: bool = False,
-    ylim: Any = None,
+    ylim: Any = (0, None),
 ) -> None:
     """Helping function to plot the aggregations of a metric."""
     ax.set_title(f"{title_prefix}{value}:")
-    if f"{value}_max" not in executors_db_df.columns:
+    if f"{value}_max" not in timeseries_db_df.columns:
         return
 
-    executors_db_df[f"{value}_max"].plot(label="max", ax=ax, ylim=ylim)
-    executors_db_df[f"{value}_median"].plot(label="median", ax=ax, ylim=ylim)
-    executors_db_df[f"{value}_mean"].plot(label="mean", ax=ax, ylim=ylim)
-    executors_db_df[f"{value}_min"].plot(label="min", ax=ax, ylim=ylim)
+    timeseries_db_df[f"{value}_max"].plot(label="max", ax=ax, ylim=ylim)
+    timeseries_db_df[f"{value}_median"].plot(label="median", ax=ax, ylim=ylim)
+    timeseries_db_df[f"{value}_mean"].plot(label="mean", ax=ax, ylim=ylim)
+    timeseries_db_df[f"{value}_min"].plot(label="min", ax=ax, ylim=ylim)
     if not pct:
-        ticks = [convert_size(x) for x in ax.get_yticks()]
-        ax.set_yticks(
-            ax.get_yticks()
-        )  # Necessary to remove UserWarning: FixedFormatter should only be used together with FixedLocator
-        ax.set_yticklabels(ticks)
+        ax_convert_size(ax)
     ax.legend()
-    plot_max_value(ax, executors_db_df[f"{value}_max"], convert_size)
+    plot_max_value(ax, timeseries_db_df[f"{value}_max"], convert_size)
+
+
+def prepare_axis(ax: matplotlib.axes.Axes) -> None:
+    """Prepare subplot ax to have no whitespace before and after the plotlines.
 
+    To call before any plotting.
+    """
+    ax.margins(x=0)
 
-def plot_db(executors_db: Dict[Any, Any]) -> None:
-    """Plot executors DB."""
-    executors_db_df = pd.DataFrame(executors_db).T
 
-    if len(executors_db_df) == 0:
+def plot_timeseries(timeseries_db_df: pd.DataFrame, title: str = None) -> matplotlib.figure.Figure:
+    """Plot timeseries DB."""
+    if len(timeseries_db_df) == 0:
         return
 
-    fig = plt.figure(constrained_layout=False, figsize=(28, 16))
+    fig = plt.figure(constrained_layout=False, figsize=(28, 16), tight_layout={"pad": 0})
     gs = fig.add_gridspec(9, 2)
+    if title is not None:
+        fig.suptitle(title, y=1)
 
     ax = fig.add_subplot(gs[0, 0])
+    prepare_axis(ax)
     ax.set_title("1. Num active executors:")
-    executors_db_df["numActive"].plot(ax=ax)
+    timeseries_db_df["numActive"].plot(ax=ax)
     ax.set_xticklabels([])
+    ax.set_ylim(bottom=0)
 
     ax = fig.add_subplot(gs[0, 1])
+    prepare_axis(ax)
     ax.set_title("2. Total memory used percentage:")
-    executors_db_df["memoryUsed_sum_pct"].plot(ax=ax, ylim=(0, 100))
-    plot_max_value(ax, executors_db_df["memoryUsed_sum_pct"])
-    executors_db_df["local_memory_pct"].plot(ax=ax, ylim=(0, 100))
-    plot_max_value(ax, executors_db_df["local_memory_pct"])
+    timeseries_db_df["memoryUsed_sum_pct"].plot(ax=ax, ylim=(0, 100))
+    plot_max_value(ax, timeseries_db_df["memoryUsed_sum_pct"])
+    timeseries_db_df["local_memory_pct"].plot(ax=ax, ylim=(0, 100))
+    plot_max_value(ax, timeseries_db_df["local_memory_pct"])
     ax.legend()
     ax.set_xticklabels([])
 
     ax = fig.add_subplot(gs[1:3, 0])
-    # mmm_plot(ax, 'memoryUsedPct', executors_db_df, '3. ', pct=True, ylim=(0,100))
-    mmm_plot(ax, "OnHeapExecutionMemory", executors_db_df, "3. Peak ")
+    prepare_axis(ax)
+    # mmm_plot(ax, 'memoryUsedPct', timeseries_db_df, '3. ', pct=True, ylim=(0,100))
+    mmm_plot(ax, "OnHeapExecutionMemory", timeseries_db_df, "3. Peak ")
     ax.set_xticklabels([])
 
     ax = fig.add_subplot(gs[1:3, 1])
-    mmm_plot(ax, "OffHeapExecutionMemory", executors_db_df, "4. Peak ")
+    prepare_axis(ax)
+    # mmm_plot(ax, "OffHeapExecutionMemory", timeseries_db_df, "4. Peak ")  # off heap is disable by default in Spark
+    ax.set_title("4. Local memory usage:")
+    timeseries_db_df["process_memory_usage"].plot(ax=ax)  # We should put only 1 ylim, the last one
+    plot_max_value(ax, timeseries_db_df["process_memory_usage"], convert_size)
+    timeseries_db_df["user_memory_usage"].plot(ax=ax, ylim=(0, None))
+    plot_max_value(ax, timeseries_db_df["user_memory_usage"], convert_size)
+    ax_convert_size(ax)
+    ax.legend()
     ax.set_xticklabels([])
 
     ax = fig.add_subplot(gs[3:5, 0])
-    mmm_plot(ax, "totalOnHeapStorageMemory", executors_db_df, "5. ")
-    # mmm_plot(ax, 'usedOnHeapStorageMemory', executors_db_df, '5. ')  # 0
+    prepare_axis(ax)
+    mmm_plot(ax, "totalOnHeapStorageMemory", timeseries_db_df, "5. ")
+    # mmm_plot(ax, 'usedOnHeapStorageMemory', timeseries_db_df, '5. ')  # 0
     ax.set_xticklabels([])
 
     ax = fig.add_subplot(gs[3:5, 1])
+    prepare_axis(ax)
     ax.set_title("6. Driver memory percentage:")
-    executors_db_df["memoryUsedPct_driver"].plot(label="driver", ax=ax)
-    plot_max_value(ax, executors_db_df["memoryUsedPct_driver"])
-    # mmm_plot(ax, 'totalOffHeapStorageMemory', executors_db_df, '6. ')
+    timeseries_db_df["memoryUsedPct_driver"].plot(label="driver", ax=ax)
+    plot_max_value(ax, timeseries_db_df["memoryUsedPct_driver"])
+    # mmm_plot(ax, 'totalOffHeapStorageMemory', timeseries_db_df, '6. ')
     ax.set_xticklabels([])
 
     ax = fig.add_subplot(gs[5:7, 0])
-    mmm_plot(ax, "ProcessTreePythonRSSMemory", executors_db_df, "7. Peak ")
+    prepare_axis(ax)
+    mmm_plot(ax, "ProcessTreePythonRSSMemory", timeseries_db_df, "7. Peak ")
     ax.set_xticklabels([])
 
     ax = fig.add_subplot(gs[5:7, 1])
-    mmm_plot(ax, "ProcessTreePythonVMemory", executors_db_df, "8. Peak ")
+    prepare_axis(ax)
+    mmm_plot(ax, "ProcessTreePythonVMemory", timeseries_db_df, "8. Peak ")
     ax.set_xticklabels([])
 
     ax = fig.add_subplot(gs[7:9, 0])
-    mmm_plot(ax, "JVMHeapMemory", executors_db_df, "9. Peak ")
+    prepare_axis(ax)
+    mmm_plot(ax, "JVMHeapMemory", timeseries_db_df, "9. Peak ")
 
     ax = fig.add_subplot(gs[7:9, 1])
-    mmm_plot(ax, "JVMOffHeapMemory", executors_db_df, "10. Peak ")
+    prepare_axis(ax)
+    mmm_plot(ax, "JVMOffHeapMemory", timeseries_db_df, "10. Peak ")
+
+    return fig
 
 
 def plot_notebook(application: sparkmon.Application) -> None:
@@ -115,3 +165,7 @@ def plot_notebook(application: sparkmon.Application) -> None:
     display.clear_output(True)
     application.plot()
     plt.show()
+
+    # To avoid memory leak
+    plt.clf()
+    plt.close()
diff --git a/src/sparkmon/utils.py b/src/sparkmon/utils.py
index b369d1b..22a878d 100644
--- a/src/sparkmon/utils.py
+++ b/src/sparkmon/utils.py
@@ -1,5 +1,24 @@
+# Copyright (c) 2021 ING Wholesale Banking Advanced Analytics
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 """Utilities functions."""
 import collections
+import getpass
 import math
 import os
 from typing import Any
@@ -8,24 +27,53 @@
 
 
 def convert_size(size_bytes) -> str:
-    """Convert bytes in human readble string."""
-    if size_bytes <= 0:
-        return f"{size_bytes:.2f}B"
+    """Convert bytes in human readble string.
+
+    Credit: https://stackoverflow.com/a/14822210/1876485
+    """
+    if size_bytes == 0:
+        return "0"
+    elif size_bytes < 0:
+        sign = -1
+        size_bytes = size_bytes * -1
+    else:
+        sign = 1
     size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
     i = int(math.floor(math.log(size_bytes, 1024)))
     p = math.pow(1024, i)
-    s = round(size_bytes / p, 2)
+    s = round(size_bytes / p, 2) * sign
     return "%s %s" % (s, size_name[i])
 
 
-def get_memory() -> Any:
-    """Get current memory usage in bytes."""
+def get_memory_process() -> Any:
+    """Get memory usage in bytes of this process running Python."""
     process = psutil.Process(os.getpid())
     return process.memory_info().rss  # in bytes
 
 
+def get_memory_user() -> Any:
+    """Get memory usage in bytes of all process of the current user."""
+    user = getpass.getuser()
+    # print(user)
+
+    total = 0
+    for p in psutil.process_iter():
+        try:
+            if p.username() == user:
+                total += p.memory_info().rss
+        except psutil.AccessDenied:
+            # For some process we might permission issue, with the following error
+            # psutil AccessDenied: psutil.AccessDenied (pid=599, name='PanGPS')
+            pass
+
+    return total
+
+
 def flatten_dict(d, parent_key="", sep="_"):
-    """Flatten dict recursively."""
+    """Flatten dict recursively.
+
+    Credit: https://stackoverflow.com/a/6027615/1876485
+    """
     items = []
     for k, v in d.items():
         new_key = parent_key + sep + k if parent_key else k


Test patch:
diff --git a/tests/test_application.py b/tests/test_application.py
index 24cbf08..c5109e3 100644
--- a/tests/test_application.py
+++ b/tests/test_application.py
@@ -32,10 +32,22 @@ def test_create_application_from_link() -> None:
     assert mon.cnt == 3
 
 
+def test_create_context_manager_application_from_link() -> None:
+    """Basic test."""
+    get_spark()
+    application = sparkmon.create_application_from_link()
+
+    with sparkmon.SparkMon(application, period=5) as mon:
+        time.sleep(14)
+
+    assert mon.cnt == 3
+
+
 def test_application_other() -> None:
     """Test parse_db."""
     spark = get_spark()
     application = sparkmon.create_application_from_spark(spark)
+    application.debug = True
 
     mon = sparkmon.SparkMon(application, period=1)
     mon.start()
diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py
index 9bdbfa0..a220ea8 100644
--- a/tests/test_callbacks.py
+++ b/tests/test_callbacks.py
@@ -60,4 +60,4 @@ def test_mlflow() -> None:
     mlflow.end_run()
     artifact_uri = file_uri_to_path(active_run.info.artifact_uri)
     assert (artifact_uri / "sparkmon/plot.png").stat().st_size > 100
-    assert (artifact_uri / "sparkmon/executors_db.csv").stat().st_size > 10
+    assert (artifact_uri / "sparkmon/timeseries.csv").stat().st_size > 10