C-Dash: Add bandwidth to iterative graphs

[csit.git] / csit.infra.dash / app / cdash / data / data.py

diff --git a/csit.infra.dash/app/cdash/data/data.py b/csit.infra.dash/app/cdash/data/data.py
index c8d5907..2b6733f 100644 (file)
--- a/csit.infra.dash/app/cdash/data/data.py
+++ b/csit.infra.dash/app/cdash/data/data.py
@@ -18,12 +18,22 @@ import logging
 import resource
 import awswrangler as wr
 import pandas as pd
 import resource
 import awswrangler as wr
 import pandas as pd

+import pyarrow as pa

 
 from yaml import load, FullLoader, YAMLError
 from datetime import datetime, timedelta
 from time import time
 from pytz import UTC
 from awswrangler.exceptions import EmptyDataFrame, NoFilesFound
 
 from yaml import load, FullLoader, YAMLError
 from datetime import datetime, timedelta
 from time import time
 from pytz import UTC
 from awswrangler.exceptions import EmptyDataFrame, NoFilesFound

+from pyarrow.lib import ArrowInvalid, ArrowNotImplementedError
+
+from ..utils.constants import Constants as C
+
+
+# If True, pyarrow.Schema is generated. See also condition in the method
+# _write_parquet_schema.
+# To generate schema, select only one data set in data.yaml file.
+GENERATE_SCHEMA = False

 
 
 class Data:
 
 
 class Data:


@@ -118,15 +128,123 @@ class Data:
 
         return file_list
 
 
         return file_list
 

+    def _validate_columns(self, data_type: str) -> str:
+        """Check if all columns are present in the dataframe.
+
+        :param data_type: The data type defined in data.yaml
+        :type data_type: str
+        :returns: Error message if validation fails, otherwise empty string.
+        :rtype: str
+        """
+        defined_columns = set()
+        for data_set in self._data_spec:
+            if data_set.get("data_type", str()) == data_type:
+                defined_columns.update(data_set.get("columns", set()))
+
+        if not defined_columns:
+            return "No columns defined in the data set(s)."
+
+        if self.data[data_type].empty:
+            return "No data."
+
+        ret_msg = str()
+        for col in defined_columns:
+            if col not in self.data[data_type].columns:
+                if not ret_msg:
+                    ret_msg = "Missing columns: "
+                else:
+                    ret_msg += ", "
+                ret_msg += f"{col}"
+        return ret_msg
+

     @staticmethod
     @staticmethod

-    def _create_dataframe_from_parquet(
-            path, partition_filter=None,
+    def _write_parquet_schema(
+            path,
+            partition_filter=None,

             columns=None,
             columns=None,

-            categories=None,

             validate_schema=False,
             last_modified_begin=None,
             last_modified_end=None,
             days=None
             validate_schema=False,
             last_modified_begin=None,
             last_modified_end=None,
             days=None

+        ) -> None:
+        """Auxiliary function to write parquet schemas. Use it instead of
+        "_create_dataframe_from_parquet" in "read_all_data".
+
+        :param path: S3 prefix (accepts Unix shell-style wildcards)
+            (e.g. s3://bucket/prefix) or list of S3 objects paths
+            (e.g. [s3://bucket/key0, s3://bucket/key1]).
+        :param partition_filter: Callback Function filters to apply on PARTITION
+            columns (PUSH-DOWN filter). This function MUST receive a single
+            argument (Dict[str, str]) where keys are partitions names and values
+            are partitions values. Partitions values will be always strings
+            extracted from S3. This function MUST return a bool, True to read
+            the partition or False to ignore it. Ignored if dataset=False.
+        :param columns: Names of columns to read from the file(s).
+        :param validate_schema: Check that individual file schemas are all the
+            same / compatible. Schemas within a folder prefix should all be the
+            same. Disable if you have schemas that are different and want to
+            disable this check.
+        :param last_modified_begin: Filter the s3 files by the Last modified
+            date of the object. The filter is applied only after list all s3
+            files.
+        :param last_modified_end: Filter the s3 files by the Last modified date
+            of the object. The filter is applied only after list all s3 files.
+        :param days: Number of days to filter.
+        :type path: Union[str, List[str]]
+        :type partition_filter: Callable[[Dict[str, str]], bool], optional
+        :type columns: List[str], optional
+        :type validate_schema: bool, optional
+        :type last_modified_begin: datetime, optional
+        :type last_modified_end: datetime, optional
+        :type days: integer, optional
+        """
+        if days:
+            last_modified_begin = datetime.now(tz=UTC) - timedelta(days=days)
+
+        df = wr.s3.read_parquet(
+            path=path,
+            path_suffix="parquet",
+            ignore_empty=True,
+            validate_schema=validate_schema,
+            use_threads=True,
+            dataset=True,
+            columns=columns,
+            partition_filter=partition_filter,
+            last_modified_begin=last_modified_begin,
+            last_modified_end=last_modified_end,
+            chunked=1
+        )
+
+        for itm in df:
+            try:
+                # Specify the condition or remove it:
+                if all((
+                        pd.api.types.is_string_dtype(itm["column_name"]),
+                        pd.api.types.is_string_dtype(itm["telemetry"][0])
+                    )):
+                    schema = pa.Schema.from_pandas(itm)
+                    pa.parquet.write_metadata(
+                        schema, f"{C.PATH_TO_SCHEMAS}_tmp_schema"
+                    )
+                    logging.info(schema.to_string(
+                        truncate_metadata=False,
+                        show_field_metadata=True,
+                        show_schema_metadata=True
+                    ))
+                    break
+            except KeyError:
+                pass
+
+    @staticmethod
+    def _create_dataframe_from_parquet(
+            path,
+            partition_filter=None,
+            columns=None,
+            validate_schema=False,
+            last_modified_begin=None,
+            last_modified_end=None,
+            days=None,
+            schema=None

         ) -> pd.DataFrame:
         """Read parquet stored in S3 compatible storage and returns Pandas
         Dataframe.
         ) -> pd.DataFrame:
         """Read parquet stored in S3 compatible storage and returns Pandas
         Dataframe.


@@ -141,8 +259,6 @@ class Data:
             extracted from S3. This function MUST return a bool, True to read
             the partition or False to ignore it. Ignored if dataset=False.
         :param columns: Names of columns to read from the file(s).
             extracted from S3. This function MUST return a bool, True to read
             the partition or False to ignore it. Ignored if dataset=False.
         :param columns: Names of columns to read from the file(s).

-        :param categories: List of columns names that should be returned as
-            pandas.Categorical.

         :param validate_schema: Check that individual file schemas are all the
             same / compatible. Schemas within a folder prefix should all be the
             same. Disable if you have schemas that are different and want to
         :param validate_schema: Check that individual file schemas are all the
             same / compatible. Schemas within a folder prefix should all be the
             same. Disable if you have schemas that are different and want to


@@ -153,14 +269,15 @@ class Data:
         :param last_modified_end: Filter the s3 files by the Last modified date
             of the object. The filter is applied only after list all s3 files.
         :param days: Number of days to filter.
         :param last_modified_end: Filter the s3 files by the Last modified date
             of the object. The filter is applied only after list all s3 files.
         :param days: Number of days to filter.

+        :param schema: Path to schema to use when reading data from the parquet.

         :type path: Union[str, List[str]]
         :type partition_filter: Callable[[Dict[str, str]], bool], optional
         :type columns: List[str], optional
         :type path: Union[str, List[str]]
         :type partition_filter: Callable[[Dict[str, str]], bool], optional
         :type columns: List[str], optional

-        :type categories: List[str], optional

         :type validate_schema: bool, optional
         :type last_modified_begin: datetime, optional
         :type last_modified_end: datetime, optional
         :type days: integer, optional
         :type validate_schema: bool, optional
         :type last_modified_begin: datetime, optional
         :type last_modified_end: datetime, optional
         :type days: integer, optional

+        :type schema: string

         :returns: Pandas DataFrame or None if DataFrame cannot be fetched.
         :rtype: DataFrame
         """
         :returns: Pandas DataFrame or None if DataFrame cannot be fetched.
         :rtype: DataFrame
         """


@@ -173,32 +290,38 @@ class Data:
                 path=path,
                 path_suffix="parquet",
                 ignore_empty=True,
                 path=path,
                 path_suffix="parquet",
                 ignore_empty=True,

+                schema=schema,

                 validate_schema=validate_schema,
                 use_threads=True,
                 dataset=True,
                 columns=columns,
                 validate_schema=validate_schema,
                 use_threads=True,
                 dataset=True,
                 columns=columns,

-                categories=categories,

                 partition_filter=partition_filter,
                 last_modified_begin=last_modified_begin,
                 partition_filter=partition_filter,
                 last_modified_begin=last_modified_begin,

-                last_modified_end=last_modified_end
+                last_modified_end=last_modified_end,
+                dtype_backend="pyarrow"

             )
             )

+

             df.info(verbose=True, memory_usage="deep")
             logging.debug(
                 f"\nCreation of dataframe {path} took: {time() - start}\n"
             )
             df.info(verbose=True, memory_usage="deep")
             logging.debug(
                 f"\nCreation of dataframe {path} took: {time() - start}\n"
             )

+        except (ArrowInvalid, ArrowNotImplementedError) as err:
+            logging.error(f"Reading of data from parquets FAILED.\n{repr(err)}")

         except NoFilesFound as err:
             logging.error(
         except NoFilesFound as err:
             logging.error(

+                f"Reading of data from parquets FAILED.\n"

                 f"No parquets found in specified time period.\n"
                 f"Nr of days: {days}\n"
                 f"last_modified_begin: {last_modified_begin}\n"
                 f"No parquets found in specified time period.\n"
                 f"Nr of days: {days}\n"
                 f"last_modified_begin: {last_modified_begin}\n"

-                f"{err}"
+                f"{repr(err)}"

             )
         except EmptyDataFrame as err:
             logging.error(
             )
         except EmptyDataFrame as err:
             logging.error(

+                f"Reading of data from parquets FAILED.\n"

                 f"No data in parquets in specified time period.\n"
                 f"Nr of days: {days}\n"
                 f"last_modified_begin: {last_modified_begin}\n"
                 f"No data in parquets in specified time period.\n"
                 f"Nr of days: {days}\n"
                 f"last_modified_begin: {last_modified_begin}\n"

-                f"{err}"
+                f"{repr(err)}"

             )
 
         return df
             )
 
         return df


@@ -214,15 +337,31 @@ class Data:
         :rtype: dict(str: pandas.DataFrame)
         """
 
         :rtype: dict(str: pandas.DataFrame)
         """
 

-        lst_trending = list()
-        lst_iterative = list()
-        lst_coverage = list()
+        data_lists = {
+            "statistics": list(),
+            "trending": list(),
+            "iterative": list(),
+            "coverage": list()
+        }

 
 

+        logging.info("\n\nReading data:\n" + "-" * 13 + "\n")

         for data_set in self._data_spec:
             logging.info(
         for data_set in self._data_spec:
             logging.info(

-                f"Reading data for {data_set['data_type']} "
-                f"{data_set['partition_name']} {data_set.get('release', '')}"
+                f"\n\nReading data for {data_set['data_type']} "
+                f"{data_set['partition_name']} {data_set.get('release', '')}\n"

             )
             )

+            schema_file = data_set.get("schema", None)
+            if schema_file:
+                try:
+                    schema = pa.parquet.read_schema(
+                        f"{C.PATH_TO_SCHEMAS}{schema_file}"
+                    )
+                except FileNotFoundError as err:
+                    logging.error(repr(err))
+                    logging.error("Proceeding without schema.")
+                    schema = None
+            else:
+                schema = None

             partition_filter = lambda part: True \
                 if part[data_set["partition"]] == data_set["partition_name"] \
                     else False
             partition_filter = lambda part: True \
                 if part[data_set["partition"]] == data_set["partition_name"] \
                     else False


@@ -230,56 +369,53 @@ class Data:
                 time_period = days
             else:
                 time_period = None
                 time_period = days
             else:
                 time_period = None

+
+            if GENERATE_SCHEMA:
+                # Generate schema:
+                Data._write_parquet_schema(
+                    path=data_set["path"],
+                    partition_filter=partition_filter,
+                    columns=data_set.get("columns", None),
+                    days=time_period
+                )
+                return
+
+            #  Read data:

             data = Data._create_dataframe_from_parquet(
                 path=data_set["path"],
                 partition_filter=partition_filter,
                 columns=data_set.get("columns", None),
             data = Data._create_dataframe_from_parquet(
                 path=data_set["path"],
                 partition_filter=partition_filter,
                 columns=data_set.get("columns", None),

-                categories=data_set.get("categories", None),
-                days=time_period
+                days=time_period,
+                schema=schema

             )
             )

-
-            if data_set["data_type"] == "statistics":
-                self._data["statistics"] = data
-            elif data_set["data_type"] == "trending":
-                lst_trending.append(data)
-            elif data_set["data_type"] == "iterative":
-                data["release"] = data_set["release"]
-                data["release"] = data["release"].astype("category")
-                lst_iterative.append(data)
-            elif data_set["data_type"] == "coverage":
+            if data_set["data_type"] in ("iterative", "coverage"):

                 data["release"] = data_set["release"]
                 data["release"] = data["release"].astype("category")
                 data["release"] = data_set["release"]
                 data["release"] = data["release"].astype("category")

-                lst_coverage.append(data)
-            else:
-                raise NotImplementedError(
-                    f"The data type {data_set['data_type']} is not implemented."
-                )

 
 

-        self._data["iterative"] = pd.concat(
-            lst_iterative,
-            ignore_index=True,
-            copy=False
-        )
-        self._data["trending"] = pd.concat(
-            lst_trending,
-            ignore_index=True,
-            copy=False
-        )
-        self._data["coverage"] = pd.concat(
-            lst_coverage,
-            ignore_index=True,
-            copy=False
-        )
+            data_lists[data_set["data_type"]].append(data)

 
 

+        logging.info(
+            "\n\nData post-processing, validation and summary:\n" +
+            "-" * 45 + "\n"
+        )

         for key in self._data.keys():
         for key in self._data.keys():

-            logging.info(
-                f"\nData frame {key}:"
-                f"\n{self._data[key].memory_usage(deep=True)}\n"
-            )
+            logging.info(f"\n\nDataframe {key}:\n")
+            self._data[key] = pd.concat(
+                data_lists[key],
+                ignore_index=True,
+                copy=False
+            )    

             self._data[key].info(verbose=True, memory_usage="deep")
             self._data[key].info(verbose=True, memory_usage="deep")

+            err_msg = self._validate_columns(key)
+            if err_msg:
+                self._data[key] = pd.DataFrame()
+                logging.error(
+                    f"Data validation FAILED.\n"
+                    f"{err_msg}\n"
+                    "Generated dataframe replaced by an empty dataframe."
+                )

 
 

-        mem_alloc = \
-            resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1000
-        logging.info(f"Memory allocation: {mem_alloc:.0f}MB")
+        mem_alloc = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1000
+        logging.info(f"\n\nMemory allocation: {mem_alloc:.0f}MB\n")

 
         return self._data
 
         return self._data