import glob
from os import mkdir
from os import path
-
+from wikiq_util import PERSISTENCE_RADIUS
#read a table
def parse_args():
parser.add_argument('-i', '--input-file', help='Tsv file of wiki edits. Supports wildcards ', required=True, type=str)
parser.add_argument('-o', '--output-dir', help='Output directory', default='./output', type=str)
# parser.add_argument('--wiki', help="Wiki name. If not provided, we will guess based on the filename.", type=str)
-# parser.add_argument('--urlencode', help="whether we need to decode urls",action="store_true")
+ parser.add_argument('--urlencode', help="whether we need to decode urls",action="store_true")
parser.add_argument('--output-format', help = "[csv, parquet] format to output",type=str)
parser.add_argument('--num-partitions', help = "number of partitions to output",type=int, default=1)
-# parser.add_argument('--ignore-input-errors', help = "ignore bad lines in input",action="store_true")
+ parser.add_argument('--schema-opt', help = 'Options for the input schema.', choices = ["basic","persistence","collapse","persistence+collapse"])
# parser.add_argument('--nodes', help = "how many hyak nodes to use", default=0, type=int)
args = parser.parse_args()
return(args)
args = parse_args()
conf = SparkConf().setAppName("Wiki Users Spark")
spark = SparkSession.builder.getOrCreate()
+
+ # test file with persistence: "../tests/tsvs/persistence_sailormoon.tsv"
files = glob.glob(args.input_file)
files = [path.abspath(p) for p in files]
- reader = spark.read
+ read_persistence = args.schema_opt in ["persistence", "persistence+collapse"]
+ read_collapse = args.schema_opt in ["collapse", "persistence+collapse"]
# going to have to do some coercing of the schema
# build a schema
struct = types.StructType().add("anon",types.StringType(),True)
struct = struct.add("articleid",types.LongType(),True)
+
+ if read_collapse is True:
+ struct = struct.add("collapsed_revs", type.IntegerType(), True)
+
struct = struct.add("date_time",types.TimestampType(), True)
struct = struct.add("deleted",types.BooleanType(), True)
struct = struct.add("editor",types.StringType(),True)
struct = struct.add("sha1", types.StringType(), True)
struct = struct.add("text_chars", types.LongType(), True)
struct = struct.add("title",types.StringType(), True)
+
+ if read_persistence is True:
+ struct = struct.add("token_revs", types.IntegerType(),True)
+ struct = struct.add("tokens_added", types.IntegerType(),True)
+ struct = struct.add("tokens_removed", types.IntegerType(),True)
+ struct = struct.add("tokens_window", types.IntegerType(),True)
+
+
+ reader = spark.read
+
df = reader.csv(files,
sep='\t',
inferSchema=False,
mode="PERMISSIVE",
schema = struct)
df = df.repartition(args.num_partitions)
+
# replace na editor ids
df = df.select('*',f.coalesce(df['editor_id'],df['editor']).alias('editor_id_or_ip'))
+ # sort by datetime
+ df = df.orderBy(df.date_time.asc())
+
+ # create our window_specs
+ ed_win = Window.orderBy('date_time').partitionBy('editor_id_or_ip')
+ art_win = Window.orderBy("date_time").partitionBy("articleid")
+
# assign which edit reverted what edit
- reverteds_df = df.filter(~ df.reverteds.isNull()).select(['revid','reverteds'])
+ reverteds_df = df.filter(~ df.reverteds.isNull()).select(['revid','reverteds','editor_id_or_ip','date_time'])
reverteds_df = reverteds_df.select("*", f.split(reverteds_df.reverteds,',').alias("reverteds_new"))
reverteds_df = reverteds_df.drop("reverteds")
reverteds_df = reverteds_df.withColumnRenamed("reverteds_new", "reverteds")
- reverteds_df = reverteds_df.select(reverteds_df.revid.alias('reverted_by'),
- f.explode(reverteds_df.reverteds).alias('reverted_id'))
- df = df.join(reverteds_df, df.revid == reverteds_df.reverted_id, how='left_outer')
- df.drop("reverted_id")
+ reverteds_df = reverteds_df.withColumn("editor_nth_revert_action", f.rank().over(ed_win))
- # sort by datetime
- df = df.orderBy(df.date_time.asc())
- win = Window.orderBy('date_time').partitionBy('editor_id_or_ip')
+ reverteds_df_explode = reverteds_df.select(reverteds_df.revid.alias('reverted_by'), f.explode(reverteds_df.reverteds).alias('reverted_id'))
+
+
+ df = df.join(reverteds_df_explode, df.revid == reverteds_df_explode.reverted_id, how='left_outer')
+ df = df.drop("reverted_id")
+ del(reverteds_df_explode)
+
+ reverteds_df = reverteds_df.select("revid","editor_nth_revert_action")
+ df = df.join(reverteds_df, on= ["revid"], how='left_outer')
+
+ del(reverteds_df)
# count reverts
- reverts_df = df.filter(df.revert==True).select(['revid','editor_id_or_ip','date_time','revert'])
- reverts_df = reverts_df.withColumn('editor_nth_revert',f.rank().over(win))
- df = df.join(reverts_df, ["revid",'editor_id_or_ip','date_time','revert'], how='left_outer')
+ reverts_df = df.filter(df.revert==True).select('revid','articleid','editor_id_or_ip','date_time','revert')
+ reverts_df = reverts_df.withColumn('editor_nth_revert',f.rank().over(ed_win))
+
+ # articles total reverts
+ reverts_df = reverts_df.withColumn('article_nth_revert',f.rank().over(art_win))
+
+ # some kind of bad work around a bug
+ # see https://issues.apache.org/jira/browse/SPARK-14948
+ reverts_df = reverts_df.select(reverts_df.revid.alias("r_revid"),'editor_nth_revert','article_nth_r
+evert')
+ df = df.join(reverts_df, df.revid == reverts_df.r_revid, how='left_outer')
+ df = df.drop("r_revid")
del(reverts_df)
# count edits
df = df.withColumn('year', f.year(df.date_time))
df = df.withColumn('month',f.month(df.date_time))
- df = df.withColumn('editor_nth_edit',f.rank().over(win))
+
+ if not read_collapse:
+ df = df.withColumn('editor_nth_edit', f.rank().over(ed_win))
+ df = df.withColumn('article_nth_edit', f.rank().over(art_win))
+ else:
+ df = df.withColumn('editor_nth_edit', f.sum("collapsed_revs").over(ed_win))
+ df = df.withColumn('article_nth_edit', f.sum("collapsed_revs").over(art_win))
+ df = df.withColumn('editor_nth_collapsed_edit', f.rank().over(ed_win))
+ df = df.withColumn('article_nth_collapsed_edit', f.rank().over(art_win))
+
+ # total editor's token_revs
+ if read_persistence:
+ df = df.withColumn("token_revs_upper", df.token_revs + df.tokens_added * (PERSISTENCE_RADIUS - df.tokens_window - 1))
+ df = df.withColumn('editor_cum_token_revs_lower', f.sum("token_revs").over(ed_win))
+ df = df.withColumn('editor_cum_token_revs_upper', f.sum("token_revs_upper").over(ed_win))
+ df = df.withColumn('article_cum_token_revs_lower', f.sum("token_revs").over(art_win))
+ df = df.withColumn('article_cum_token_revs_upper', f.sum("token_revs_upper").over(art_win))
+ df = df.withColumn('editor_cum_tokens_added', f.sum("tokens_added").over(ed_win))
+ df = df.withColumn('article_cum_tokens_removed', f.sum("tokens_removed").over(art_win))
# output
if not path.exists(args.output_dir):