Merge branch 'ci-investigations' into '5-add-ci-integration-package-conda-env-wip'

Update Gitlab CI MR

See merge request !10

.bumpversion.cfg

@@ -4,7 +4,7 @@ current_version = 0.1.0
 # Create a commit using git when true.
 commit = False
 # Whether to create a git tag, that is the new version,
-# prefixed with the character "r". 
+# prefixed with the character "r".
 # Don't forget to git-push with the --tags flag.
 tag = False
 

.gitignore

 data/
 *.egg-info
 *.pyc
-__pycache__/
\ No newline at end of file
+__pycache__/
+
+
+.cache/
+.conda/
+.coverage
+.local/
+build/
+dist/

.gitlab-ci.yml

@@ -17,12 +17,12 @@
 # publish_conda_env package the project and its dependencis in a conda distribution.
 # This is a relocatable virtual environment, that can be used to
 # ship the project to spark workers.
-# The knowledge-gaps archive is available at 
-# https://gitlab.wikimedia.org/repos/research/knowledge-gaps/-/packages/86 
+# The knowledge-gaps archive is available at
+# https://gitlab.wikimedia.org/repos/research/knowledge-gaps/-/packages/86
 # Currently this job must be manually trigger from Gitlab's Pipeline ui.
 include:
   - project: 'repos/data-engineering/workflow_utils'
-    ref: 'v0.4.0'
+    ref: 'v0.5.0'
     file: '/gitlab_ci_templates/pipelines/conda_artifact_repo.yml'
 
 # A set of commands that will be executed before each job.
@@ -62,7 +62,7 @@ typecheck:
 
 publish_conda_env:
   before_script:
-    - !reference [.conda_setup_script] 
+    - !reference [.conda_setup_script]
     - apt install -y libkrb5-dev libsasl2-dev gcc g++
   script:
     - !reference [.conda_dist_publish_script]

Dockerfile

-# A Docker file to build a buster image with python 3 and miniconda.
-# It's based atop Wikimedia's official python3 image, and its runtime
-# matches the container that runs Gitlab CI.
+# based on https://gitlab.wikimedia.org/repos/data-engineering/workflow_utils/-/blob/main/gitlab_ci_templates/lib/conda.yml
 
-FROM docker-registry.wikimedia.org/python3-build-buster:0.1.0-20220313
+FROM docker-registry.wikimedia.org/wikimedia-buster:20210523
 
-RUN apt update
-RUN apt install -y curl gpg
-RUN curl https://repo.anaconda.com/pkgs/misc/gpgkeys/anaconda.asc | gpg --dearmor > /tmp/conda.gpg 
-RUN install -o root -g root -m 644 /tmp/conda.gpg /usr/share/keyrings/conda-archive-keyring.gpg
-RUN gpg --keyring /usr/share/keyrings/conda-archive-keyring.gpg --no-default-keyring --fingerprint 34161F5BF5EB1D4BFBBB8F0A8AEB4F8B29D82806
-RUN echo "deb [arch=amd64 signed-by=/usr/share/keyrings/conda-archive-keyring.gpg] https://repo.anaconda.com/pkgs/misc/debrepo/conda stable main" > /etc/apt/sources.list.d/conda.list
+ARG wmf_workflow_utils=v0.5.0
 
-# Install a number of deps necessary to build wmfdata (and other).
-RUN apt update && apt install -y gcc g++ make curl libkrb5-dev conda libsasl2-dev
-# Install tox in the docker image (rather than declare it in requirements.txt)
-# to match Gitlab's runtime.
-RUN pip3 install tox==3.24.4
+RUN echo "deb http://apt.wikimedia.org/wikimedia buster-wikimedia thirdparty/conda" >> /etc/apt/sources.list.d/wikimedia.list
+RUN apt update
+RUN apt install -y curl gcc g++ gpg git make ca-certificates conda
+RUN apt install -y libkrb5-dev libsasl2-dev
+SHELL ["/bin/bash", "-c"]
+RUN source /opt/conda/etc/profile.d/conda.sh && conda activate
+# RUN pip install tox==3.24.4
+# RUN pip install git+https://gitlab.wikimedia.org/repos/data-engineering/workflow_utils.git@${wmf_workflow_utils}
+RUN /opt/conda/bin/pip install tox==3.24.4
+RUN /opt/conda/bin/pip install git+https://gitlab.wikimedia.org/repos/data-engineering/workflow_utils.git@${wmf_workflow_utils}
\ No newline at end of file

Makefile

 # This Makefile provides some boilerplate to run conda-dist,
 # test, lint and typechecking targets.
-# A docker image is available to perform all CI steps 
-# locally, in an enviroment that matches Gitlab's CI container. 
-# 
+# A docker image is available to perform all CI steps
+# locally, in an enviroment that matches Gitlab's CI container.
+#
 # Example:
 #
 # $ make docker-conda
@@ -26,10 +26,6 @@ DOCKER_PLATFORM := linux/amd64
 PROJECT_NAME := knowledge-gaps
 # Do not modify PROJECT_VERSION manually. Use bump2version instead.
 PROJECT_VERSION := 0.1.0
-# TODO(gmodena, 2022-03-25): when development stabilizes we should track 
-# a release tag / wheel instead of main. I encountered occasional conda-pack 
-# failures while tracking main, that I was not able to consistently reproduce.
-WMF_WORKFLOW_UTILS := git+https://gitlab.wikimedia.org/repos/data-engineering/workflow_utils.git@main
 CONDA_DIST := ./dist/${PROJECT_NAME}-${PROJECT_VERSION}.conda-${GIT_BRANCH}-${GIT_COMMIT_HASH}
 
 REQUIREMENTS_FILE := requirements_dev.txt
@@ -47,7 +43,7 @@ DOCKER_CMD := docker run -it \
 			  -e SKIP_DOCKER=true \
 			  -w /root ${DOCKER_IMG}
 
-# The research/miniconda3 image is required to run 
+# The research/miniconda3 image is required to run
 # CI targets in a docker container.
 env: docker-conda
 test: docker-conda
@@ -57,10 +53,10 @@ endif
 
 # Create a conda dist archive, containing this project and all
 # its dependencies.
-env: 
-	${DOCKER_CMD} bash -c "source /opt/conda/etc/profile.d/conda.sh; \
-		/opt/conda/bin/pip install ${WMF_WORKFLOW_UTILS}; \
-		/opt/conda/bin/conda-dist --dist-env-prefix=${CONDA_DIST}"
+env:
+	${DOCKER_CMD} bash -c "source /opt/conda/etc/profile.d/conda.sh && \
+	conda activate && \
+	conda-dist --dist-env-prefix=${CONDA_DIST}"
 
 # Run the pytest suite.
 test:

README.md

@@ -4,6 +4,57 @@
 1. Install dependencies: `pip install -r requirements_dev.txt`
 2. Enable Git hooks: `git config --local core.hooksPath .githooks/`
 
+## Running
+### Metrics
+Activate your Conda environment, install the current package, and pack
+the environment like so:
+
+```bash
+pip install .
+conda pack --ignore-editable-packages -o environment.tar.gz
+```
+
+Then run:
+
+```bash
+PYSPARK_DRIVER_PYTHON=python \
+PYSPARK_PYTHON=./environment/bin/python \
+    spark2-submit \
+    --master yarn \
+    --driver-memory 2G \
+    --executor-memory 8G \
+    --archives environment.tar.gz#environment \
+    knowledge_gaps/metrics.py \
+    20220101 20220131 \
+    --projects enwiki \
+    --pageviews_table bmansurov.pageview_hourly_2022_01 \
+    --wikipedia_pages_table wikipedia_pages_2022_01 \
+    --mediawiki_snapshot 2022-01 \
+    --wikidata_snapshot 2022-01-24 \
+    --save_table bmansurov.metrics_20220422
+```
+
+* run locally:
+```bash
+spark2-submit --master local knowledge_gaps/content_gap_pipeline.py \
+    --content_gap <content gap> \
+    --output <table name to save> \
+    --wikidata_qid_table <saved wikidata qid table> \
+    --wikidata_prop_table <saved wikidata property table> \
+    --wikipedia_page_table <saved wikipedia page table>
+```
+
+* run on yarn:
+```bash
+export PYSPARK_DRIVER_PYTHON=python
+export PYSPARK_PYTHON=./venv/bin/python
+spark2-submit --master yarn --archives <conda archive file>#venv knowledge_gaps/content_gap_pipeline.py \
+    --content_gap <content gap> \
+    --output <table name to save> \
+    --projects <wiki list> \
+    --mediawiki_snapshot <YYYY-MM> \
+    --wikidata_snapshot <YYYY-MM-DD>
+```
 
 ## Testing
 A [CI Pipeline](https://gitlab.wikimedia.org/repos/research/knowledge-gaps/-/pipelines) is triggered
@@ -12,14 +63,14 @@ after each `push` and merge request.
 Under the hood we use [tox](https://pypi.org/project/tox/) for test automation.
 
 `Makefile` provides some boilerplate to run conda-dist, test, lint and typechecking targets.
-A docker image is available to perform all CI steps locally, 
-in an enviroment that matches Gitlab's CI container.                                                         
+A docker image is available to perform all CI steps locally,
+in an enviroment that matches Gitlab's CI container.
 
-Run                                                                                                              
+Run
 ```
-make docker-conda                                                                                                  
+make docker-conda
 ```
-to generate a suitable python3 + miniconda docker image.                                                              
+to generate a suitable python3 + miniconda docker image.
 
 
 - generate a `x86_64-linux` conda distrirubtion runt time with
@@ -62,7 +113,7 @@ To help propagate version bumps across multiple file locations (`setup.py`, `Mak
 
 For example, the following command
 ```
-bump2version patch 
+bump2version patch
 ```
 will increase the patch version in `setup.py` and `Makefile`.
 Changes will need to be manually committed to git.

conda-environment.yaml

 # This file is required to create a conda distribution
 # with conda-dist.
-channels:
-  - defaults
-  - conda-forge
+# channels:
+#   - defaults
+#   - conda-forge
 dependencies:
   - python=3.7
-  - python-gssapi
+  # - python-gssapi

interactive/metrics.py

@@ -3,7 +3,7 @@ from datetime import datetime, timedelta
 import pyspark.sql.functions as F
 
 import wmfdata as wmf
-spark_session = wmf.spark.get_session(
+spark = wmf.spark.get_session(
     type='local',
     app_name="knowledge-gaps"
 )
@@ -17,7 +17,7 @@ today = datetime.today()
 ninety_days_earlier = today - timedelta(days=90)
 
 pageviews_df = article_features.extract_pageviews(
-    spark_session,
+    spark,
     ninety_days_earlier,
     today,
     projects,
@@ -26,12 +26,12 @@ pageviews_df = article_features.extract_pageviews(
 print(pageviews_df.head(3))
 
 
-def get_pages_df(spark_session, table='aikochou.pages_20220310'):
+def get_pages_df(spark, table='aikochou.pages_20220310'):
     query = f"SELECT * FROM {table}"
-    return spark_session.sql(query)
+    return spark.sql(query)
 
 
-pages_df = get_pages_df(spark_session)
+pages_df = get_pages_df(spark)
 print(pages_df.head(3))
 
 # Here we can add content gaps to pages_df, etc.

knowledge_gaps/aggregation_pipeline.py

+import argparse
+from pyspark.sql import SparkSession
+import pyspark.sql.functions as F
+from pyspark.sql.functions import col
+from pyspark.sql.types import IntegerType, ArrayType, FloatType
+import pandas as pd # type: ignore
+import numpy as np # type: ignore
+import re
+
+def cml_sum(x):
+    return np.cumsum(np.array(x))
+
+cml_sum_udf = F.udf(lambda x: cml_sum(x).tolist(), ArrayType(IntegerType()))
+
+def percentage(x, y):
+    return [x[i]/y[i]*100 if y[i]!=0 else None for i in range(len(x))]
+
+percentage_udf = F.udf(lambda x, y: percentage(x, y), ArrayType(FloatType()))
+
+def rename_cols(df, prefix, other_cols):
+    new_col_name = [col(col_name).alias(prefix + re.search(r"(\d+)", col_name).group(0))
+                    for col_name in df.columns if col_name.startswith("sum")]
+    return df.select(*other_cols, *new_col_name)
+
+def add_percentages(df, metric_name):
+    df = df.withColumn(metric_name+'_pct', percentage_udf(col(metric_name), col(metric_name+'_sum')))
+    df = df.withColumn(metric_name+'_cml_pct', percentage_udf(col(metric_name+'_cml'), col(metric_name+'_sum_cml')))
+    return df
+
+def get_df(spark, table):
+    "Temporary function for easy pages retrieval during development."
+    query = f"SELECT * FROM {table}"
+    return spark.sql(query)
+
+def main(args):
+    spark = SparkSession.builder.getOrCreate()
+    tbl = get_df(spark, args.content_gap_table)
+
+    if args.period == "monthly":
+        tbl = tbl.withColumn("month", F.substring("create_timestamp", 1, 7))
+        t_range = [m.strftime('%Y-%m') for m in pd.date_range(start=args.start_time, end=args.end_time, freq='M')]
+        expr = [F.when(F.col("month") == t, 1).otherwise(0).alias(args.metric+'_'+t) for t in t_range]
+    
+    elif args.period == "yearly":
+        tbl = tbl.withColumn("year", F.substring("create_timestamp", 1, 4))
+        t_range = [str(y) for y in range(int(args.start_time), int(args.end_time)+1)]
+        expr = [F.when(F.col("year") == t, 1).otherwise(0).alias(args.metric+'_'+t) for t in t_range]
+
+    tbl = tbl.select("wiki_db",
+                     "page_id",
+                     "qitem_id",
+                     "page_title",
+                     "is_human",
+                     args.content_gap,
+                     *expr).cache()
+
+    t_col_names = [args.metric+'_'+t for t in t_range]
+    aggr_exprs = {col_name: "sum" for col_name in t_col_names}
+    sum_col_names = [args.metric+'_sum_'+t for t in t_range]
+
+    if args.content_gap in ("gender", "sexual_orientation"):
+        ## metric ##        
+        exploded = (tbl
+                    .where(col("is_human")==True)
+                    .withColumn("gap", F.explode(args.content_gap))
+                    .select("wiki_db",
+                            "page_id",
+                            "qitem_id",
+                            "page_title",
+                            *t_col_names,
+                            F.col("gap."+args.content_gap)
+                                .alias(args.content_gap)).cache())
+
+        grouped = exploded.groupBy("wiki_db", args.content_gap).agg(aggr_exprs)
+        grouped = rename_cols(grouped, args.metric+'_', ["wiki_db", args.content_gap])
+        grouped = grouped.withColumn(args.metric, F.array(*t_col_names)).drop(*t_col_names) # arrayize
+        grouped = grouped.withColumn(args.metric+'_cml', cml_sum_udf(col(args.metric))) # cumulative
+        
+        ## biography articles ##
+        bio_total = tbl.where(col("is_human")==True).groupBy("wiki_db").agg(aggr_exprs)
+        bio_total = rename_cols(bio_total, args.metric+'_sum_', ["wiki_db"])
+        bio_total = bio_total.withColumn(args.metric+'_sum', F.array(*sum_col_names)).drop(*sum_col_names) # arrayize
+        bio_total = bio_total.withColumn(args.metric+'_sum_cml', cml_sum_udf(col(args.metric+'_sum'))) # cumulative
+        
+        joined = grouped.join(bio_total, "wiki_db", "left")
+        result = add_percentages(joined, args.metric)
+
+        #joined.write.mode('overwrite').saveAsTable('aikochou.article_count_20220401')
+        result.show()
+
+    elif args.content_gap == "geographic":
+        ## all articles ##
+        total = tbl.groupBy("wiki_db").agg(aggr_exprs)
+        total = rename_cols(total, args.metric+'_sum_', ["wiki_db"])
+        total = total.withColumn(args.metric+'_sum', F.array(*sum_col_names)).drop(*sum_col_names) # arrayize
+        total = total.withColumn(args.metric+'_sum_cml', cml_sum_udf(col(args.metric+'_sum'))) # cumulative
+
+        ## metric ##
+        model = "geospatial_model"
+
+        if model == "cultural_model":
+            ### geographic.cultural_model.region ###
+            exploded = (tbl
+                        .withColumn('gap', F.explode('geographic.cultural_model'))
+                        .select("wiki_db",
+                                "page_id",
+                                "qitem_id",
+                                "page_title",
+                                *t_col_names,
+                                F.col("gap.region").alias("region")).cache())
+
+            grouped = exploded.groupBy("wiki_db", "region").agg(aggr_exprs)
+            grouped = rename_cols(grouped, args.metric+'_', ["wiki_db", "region"])
+            grouped = grouped.withColumn(args.metric, F.array(*t_col_names)).drop(*t_col_names) # arrayize
+            grouped = grouped.withColumn(args.metric+'_cml', cml_sum_udf(col(args.metric))) # cumulative
+            joined = grouped.join(total, "wiki_db", "left")
+            result = add_percentages(joined, args.metric)
+            result.show()
+ 
+        elif model == "geospatial_model":
+            ### geographic.geospatial_model ###
+            exploded = (tbl
+                        .withColumn('gap', F.explode('geographic.geospatial_model'))
+                        .select("wiki_db",
+                                "page_id",
+                                "qitem_id",
+                                "page_title",
+                                *t_col_names,
+                                F.col('gap.geocode.country_code').alias('country_code'),
+                                F.col('gap.continent').alias('continent'),
+                                F.col('gap.sub_continent').alias('sub_continent')).cache())
+
+            grouped = exploded.groupBy("wiki_db", "country_code", "continent", "sub_continent").agg(aggr_exprs)
+            
+            grouped = rename_cols(grouped, args.metric+'_', ["wiki_db",
+                                                             "country_code",
+                                                             "continent",
+                                                             "sub_continent"]).cache()
+            categories = ["sub_continent", "continent"]
+            geospatial = []
+
+            for category in categories:
+                cat_grouped = grouped.groupBy("wiki_db", category).agg(aggr_exprs)
+                cat_grouped = rename_cols(cat_grouped, args.metric+'_', ["wiki_db", category])
+                cat_grouped = cat_grouped.withColumn(args.metric, F.array(*t_col_names)).drop(*t_col_names) # arrayize
+                cat_grouped = cat_grouped.withColumn(args.metric+'_cml', cml_sum_udf(col(args.metric))) # cumulative
+                geospatial.append(cat_grouped)
+            
+            grouped = grouped.drop(*categories)            
+            grouped = grouped.withColumn(args.metric, F.array(*t_col_names)).drop(*t_col_names) # arrayize
+            grouped = grouped.withColumn(args.metric+'_cml', cml_sum_udf(col(args.metric))) # cumulative
+            geospatial.append(grouped)
+
+            geospatial = [x.join(total, "wiki_db", "left") for x in geospatial]
+            results = [add_percentages(x, args.metric) for x in geospatial]            
+            for r in results:
+                r.show()
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--content_gap',
+                        default='geographic',
+                        help='gender, sexual_orientation, geographic or time')
+    parser.add_argument('--metric',
+                        default='article',
+                        help='article or pageview')
+    parser.add_argument('--period',
+                        default='yearly',
+                        help='"monthly" or "yearly"')
+    parser.add_argument('--start_time',
+                        default='2001',
+                        help='Count articles starting this year'
+                        ' or month(%Y-%m)')
+    parser.add_argument('--end_time',
+                        default='2021',
+                        help='Count articles ending this year'
+                        ' or month(%Y-%m)')
+    parser.add_argument('--content_gap_table',
+                        default='aikochou.content_gap_feature_20220401',
+                        help='Content gap feature table.')
+
+    args = parser.parse_args()
+    print(args)
+    
+    main(args)
\ No newline at end of file

knowledge_gaps/article_features.py

@@ -64,10 +64,10 @@ def extract_templates(wikicode):
         return None
 
 
-@F.udf(returnType="struct<links:array<string>,num_refs:int,headings:array<string>,templates:array<string>>")
 def extract_wikitext_attributes(wikitext):
     """
     The python functions at the top operate on the wikicode allows us to write an udf that parses the wikitext only once.
+    returnType: "struct<links:array<string>,num_refs:int,headings:array<string>,templates:array<string>>"
     """
     features = {}
     wikicode = mwparserfromhell.parse(wikitext)
@@ -78,7 +78,35 @@ def extract_wikitext_attributes(wikitext):
     return Row(**features)
 
 
-def extract_pageviews(spark_session, start_date, end_date,
+def wikitext_df(spark, mediawiki_snapshot, projects=None):
+    """
+    retrieve wikitext for all the wikipedia pages for given projects. default is all wiki projects.
+    root
+     |-- wiki_db: string (nullable = true)
+     |-- page_id: long (nullable = true)
+     |-- page_title: string (nullable = true)
+     |-- revision_id: long (nullable = true)
+     |-- revision_text: string (nullable = true)
+    """
+    query = f"""
+        SELECT mw.wiki_db, mw.page_id, mw.page_title, mw.revision_id, mw.revision_text
+        FROM wmf.mediawiki_wikitext_current mw
+        LEFT JOIN wmf_raw.mediawiki_page p
+            ON p.page_id = mw.page_id
+            AND p.wiki_db = mw.wiki_db
+        WHERE mw.snapshot = '{mediawiki_snapshot}'
+        AND mw.page_namespace = 0
+        AND p.snapshot = '{mediawiki_snapshot}'
+            AND p.page_namespace = 0
+            AND p.page_is_redirect = 0
+        """
+    df = spark.sql(query)
+    if projects:
+        df = df.where(F.col('wiki_db').isin(projects))
+    return df
+
+
+def extract_pageviews(spark, start_date, end_date,
                       projects=None, table="wmf.pageview_hourly"):
     """Extract the number of pageviews between START_DATE and END_DATE
     for PROJECTS. Supply a smaller TABLE for faster queries during
@@ -86,7 +114,7 @@ def extract_pageviews(spark_session, start_date, end_date,
 
     Parameters
     ----------
-    spark_session : SparkSession
+    spark : SparkSession
 
     start_date : datetime.datetime
         Start date for counting pageviews.
@@ -131,7 +159,7 @@ def extract_pageviews(spark_session, start_date, end_date,
         GROUP BY
           project, page_title, page_id, year, month, day
     """
-    df = spark_session.sql(query)
+    df = spark.sql(query)
     if projects:
         df = df.where(F.col('wiki_db').isin(projects))
     return df

knowledge_gaps/content_gap_pipeline.py

+import argparse
+from pyspark.sql import SparkSession
+import pyspark.sql.functions as F
+from knowledge_gaps import util, func
+
+def get_df(spark, table):
+    "Temporary function for easy pages retrieval during development."
+    query = f"SELECT * FROM {table}"
+    return spark.sql(query)
+
+
+def main(args, f):
+    spark = SparkSession.builder.getOrCreate()    
+
+    if args.wikidata_qid_table:
+        qids = get_df(spark, args.wikidata_qid_table)
+    else:
+        qids = func.wikidata_relevant_qitems_df(spark,
+                                                args.wikidata_snapshot,
+                                                args.mediawiki_snapshot,
+                                                args.projects)
+    if args.wikidata_prop_table:
+        prop = get_df(spark, args.wikidata_prop_table)
+    else:
+        prop = func.wikidata_relevant_properties(spark,
+                                                 args.wikidata_snapshot,
+                                                 qids).cache()
+    if args.wikipedia_page_table:
+        pages = get_df(spark, args.wikipedia_page_table)
+    else:
+        pages = func.wikipedia_pages_df(spark,
+                                        args.mediawiki_snapshot,
+                                        args.wikidata_snapshot,
+                                        args.projects)
+    
+    qids = func.append_is_human(qids, prop)
+    if args.content_gap == "gender":
+        gender_cat = util.get_category(f['gender_cat'][0])
+        
+        qid_to_gender = spark.createDataFrame(
+                                gender_cat, ['qid', 'gender'])
+
+        qids = func.append_gender_feature(qids,
+                                        prop,
+                                        qid_to_gender)
+    
+    elif args.content_gap == "sexual_orientation":
+        sexual_orientation_cat = util.get_category(f['sexual_orientation_cat'][0])
+        
+        qid_to_label = spark.createDataFrame(sexual_orientation_cat, 
+                                            ['qid', 'sexual_orientation'])
+
+        qids = func.append_sexual_orientation_feature(qids, 
+                                                    prop, 
+                                                    qid_to_label)
+    
+    elif args.content_gap == "geographic":
+        continent = util.get_continent(f['continent'][0])
+        
+        cc_to_continent = spark.createDataFrame(continent, ['country_name',
+                                                            'country_code',
+                                                            'continent',
+                                                            'sub_continent'])
+
+
+        region_cat = util.get_qid_to_region(f['region_qids'][0], f['country_aggr'][0])
+
+        qid_to_region = spark.createDataFrame(region_cat.items(), 
+                                            ['qid', 'region'])
+        
+        region_prop = util.get_property(f['country_prop'][0])
+        
+        region_prop_list = [p[0] for p in region_prop]
+
+        qids = func.append_geographic_feature(qids, 
+                                            prop, 
+                                            qid_to_region,
+                                            cc_to_continent, 
+                                            region_prop_list)
+    
+    elif args.content_gap == "time":
+        time_prop = util.get_property(f['time_prop'][0])
+
+        time_prop_list = [p[0] for p in time_prop]
+
+        qids = func.append_time_feature(qids, 
+                                        prop, 
+                                        time_prop_list)
+
+    prop.unpersist()
+