+++ /dev/null
-#!/usr/bin/env python3
-
-import pyspark
-from pyspark.sql import functions as f
-from pyspark.sql.types import *
-from pyspark import SparkConf, SparkContext
-from pyspark.sql import SparkSession, SQLContext
-
-conf = SparkConf().setAppName("Reddit comments to parquet")
-conf = conf.set('spark.sql.crossJoin.enabled',"true")
-
-spark = SparkSession.builder.getOrCreate()
-sc = spark.sparkContext
-
-globstr = "/gscratch/comdata/raw_data/reddit_dumps/comments/RC_20*.bz2"
-
-import re
-import glob
-import json
-from subprocess import Popen, PIPE
-from datetime import datetime
-import pandas as pd
-from multiprocessing import Pool
-
-def open_fileset(globstr):
- files = glob.glob(globstr)
- for fh in files:
- print(fh)
- lines = open_input_file(fh)
- for line in lines:
- yield json.loads(line)
-
-def open_input_file(input_filename):
- if re.match(r'.*\.7z$', input_filename):
- cmd = ["7za", "x", "-so", input_filename, '*']
- elif re.match(r'.*\.gz$', input_filename):
- cmd = ["zcat", input_filename]
- elif re.match(r'.*\.bz2$', input_filename):
- cmd = ["bzcat", "-dk", input_filename]
-
- elif re.match(r'.*\.bz', input_filename):
- cmd = ["bzcat", "-dk", input_filename]
- elif re.match(r'.*\.xz', input_filename):
- cmd = ["xzcat",'-dk',input_filename]
- try:
- input_file = Popen(cmd, stdout=PIPE).stdout
- except NameError:
- input_file = open(input_filename, 'r')
- return input_file
-
-def include_row(comment, subreddits_to_track = []):
-
- subreddit = comment['subreddit'].lower()
-
- return subreddit in subreddits_to_track
-
-def parse_comment(comment, names= None):
- if names is None:
- names = ["id","subreddit","link_id","parent_id","created_utc","author","ups","downs","score","edited","subreddit_type","subreddit_id","stickied","is_submitter","body","error"]
-
- try:
- comment = json.loads(comment)
- except json.decoder.JSONDecodeError as e:
- print(e)
- print(comment)
- row = [None for _ in names]
- row[-1] = "json.decoder.JSONDecodeError|{0}|{1}".format(e,comment)
- return tuple(row)
-
- row = []
- for name in names:
- if name == 'created_utc':
- row.append(datetime.fromtimestamp(int(comment['created_utc']),tz=None))
- elif name == 'edited':
- val = comment[name]
- if type(val) == bool:
- row.append(val)
- row.append(None)
- else:
- row.append(True)
- row.append(datetime.fromtimestamp(int(val),tz=None))
- elif name == "time_edited":
- continue
- elif name not in comment:
- row.append(None)
-
- else:
- row.append(comment[name])
-
- return tuple(row)
-
-
-# conf = sc._conf.setAll([('spark.executor.memory', '20g'), ('spark.app.name', 'extract_reddit_timeline'), ('spark.executor.cores', '26'), ('spark.cores.max', '26'), ('spark.driver.memory','84g'),('spark.driver.maxResultSize','0'),('spark.local.dir','/gscratch/comdata/spark_tmp')])
-
-sqlContext = pyspark.SQLContext(sc)
-
-comments = sc.textFile(globstr)
-
-schema = StructType().add("id", StringType(), True)
-schema = schema.add("subreddit", StringType(), True)
-schema = schema.add("link_id", StringType(), True)
-schema = schema.add("parent_id", StringType(), True)
-schema = schema.add("created_utc", TimestampType(), True)
-schema = schema.add("author", StringType(), True)
-schema = schema.add("ups", LongType(), True)
-schema = schema.add("downs", LongType(), True)
-schema = schema.add("score", LongType(), True)
-schema = schema.add("edited", BooleanType(), True)
-schema = schema.add("time_edited", TimestampType(), True)
-schema = schema.add("subreddit_type", StringType(), True)
-schema = schema.add("subreddit_id", StringType(), True)
-schema = schema.add("stickied", BooleanType(), True)
-schema = schema.add("is_submitter", BooleanType(), True)
-schema = schema.add("body", StringType(), True)
-schema = schema.add("error", StringType(), True)
-
-rows = comments.map(lambda c: parse_comment(c, schema.fieldNames()))
-#!/usr/bin/env python3
-
-df = sqlContext.createDataFrame(rows, schema)
-
-df = df.withColumn("subreddit_2", f.lower(f.col('subreddit')))
-df = df.drop('subreddit')
-df = df.withColumnRenamed('subreddit_2','subreddit')
-
-df = df.withColumnRenamed("created_utc","CreatedAt")
-df = df.withColumn("Month",f.month(f.col("CreatedAt")))
-df = df.withColumn("Year",f.year(f.col("CreatedAt")))
-df = df.withColumn("Day",f.dayofmonth(f.col("CreatedAt")))
-df = df.withColumn("subreddit_hash",f.sha2(f.col("subreddit"), 256)[0:3])
-
-# cache so we don't have to extract everythin twice
-df = df.cache()
-
-df2 = df.sort(["subreddit","author","link_id","parent_id","Year","Month","Day"],ascending=True)
-df2.write.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet", partitionBy=["Year",'Month'],mode='overwrite')
-
-df3 = df.sort(["author","CreatetdAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
-df3.write.parquet("/gscratch/comdata/output/reddit_comments_by_author.parquet", partitionBy=["Year",'Month'],mode='overwrite')
--- /dev/null
+#!/usr/bin/env bash
+
+echo "!#/usr/bin/bash" > job_script.sh
+echo "source $(pwd)/../bin/activate" >> job_script.sh
+echo "python3 $(pwd)/comments_2_parquet_part1.py" >> job_script.sh
+
+srun -p comdata -A comdata --nodes=1 --mem=120G --time=48:00:00 job_script.sh
+
+start_spark_and_run.sh 1 $(pwd)/comments_2_parquet_part2.py
--- /dev/null
+#!/usr/bin/env python3
+import json
+from datetime import datetime
+from multiprocessing import Pool
+from itertools import islice
+from helper import find_dumps, open_fileset
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+globstr_base = "/gscratch/comdata/reddit_dumps/comments/RC_20*"
+
+def parse_comment(comment, names= None):
+ if names is None:
+ names = ["id","subreddit","link_id","parent_id","created_utc","author","ups","downs","score","edited","subreddit_type","subreddit_id","stickied","is_submitter","body","error"]
+
+ try:
+ comment = json.loads(comment)
+ except json.decoder.JSONDecodeError as e:
+ print(e)
+ print(comment)
+ row = [None for _ in names]
+ row[-1] = "json.decoder.JSONDecodeError|{0}|{1}".format(e,comment)
+ return tuple(row)
+
+ row = []
+ for name in names:
+ if name == 'created_utc':
+ row.append(datetime.fromtimestamp(int(comment['created_utc']),tz=None))
+ elif name == 'edited':
+ val = comment[name]
+ if type(val) == bool:
+ row.append(val)
+ row.append(None)
+ else:
+ row.append(True)
+ row.append(datetime.fromtimestamp(int(val),tz=None))
+ elif name == "time_edited":
+ continue
+ elif name not in comment:
+ row.append(None)
+
+ else:
+ row.append(comment[name])
+
+ return tuple(row)
+
+
+# conf = sc._conf.setAll([('spark.executor.memory', '20g'), ('spark.app.name', 'extract_reddit_timeline'), ('spark.executor.cores', '26'), ('spark.cores.max', '26'), ('spark.driver.memory','84g'),('spark.driver.maxResultSize','0'),('spark.local.dir','/gscratch/comdata/spark_tmp')])
+
+dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments"
+
+files = list(find_dumps(dumpdir, base_pattern="RC_20*.*"))
+
+pool = Pool(28)
+
+stream = open_fileset(files)
+
+N = 100000
+
+rows = pool.imap_unordered(parse_comment, stream, chunksize=int(N/28))
+
+schema = pa.schema([
+ pa.field('id', pa.string(), nullable=True),
+ pa.field('subreddit', pa.string(), nullable=True),
+ pa.field('link_id', pa.string(), nullable=True),
+ pa.field('parent_id', pa.string(), nullable=True),
+ pa.field('created_utc', pa.timestamp('ms'), nullable=True),
+ pa.field('author', pa.string(), nullable=True),
+ pa.field('ups', pa.int64(), nullable=True),
+ pa.field('downs', pa.int64(), nullable=True),
+ pa.field('score', pa.int64(), nullable=True),
+ pa.field('edited', pa.bool_(), nullable=True),
+ pa.field('time_edited', pa.timestamp('ms'), nullable=True),
+ pa.field('subreddit_type', pa.string(), nullable=True),
+ pa.field('subreddit_id', pa.string(), nullable=True),
+ pa.field('stickied', pa.bool_(), nullable=True),
+ pa.field('is_submitter', pa.bool_(), nullable=True),
+ pa.field('body', pa.string(), nullable=True),
+ pa.field('error', pa.string(), nullable=True),
+])
+
+with pq.ParquetWriter("/gscratch/comdata/output/reddit_comments.parquet_temp",schema=schema,compression='snappy',flavor='spark') as writer:
+ while True:
+ chunk = islice(rows,N)
+ pddf = pd.DataFrame(chunk, columns=schema.names)
+ table = pa.Table.from_pandas(pddf,schema=schema)
+ if table.shape[0] == 0:
+ break
+ writer.write_table(table)
+
+ writer.close()
--- /dev/null
+#!/usr/bin/env python3
+
+# spark script to make sorted, and partitioned parquet files
+
+from pyspark.sql import functions as f
+from pyspark.sql import SparkSession
+
+spark = SparkSession.builder.getOrCreate()
+
+df = spark.read.parquet("/gscratch/comdata/output/reddit_comments.parquet_temp2")
+
+df = df.withColumn("subreddit_2", f.lower(f.col('subreddit')))
+df = df.drop('subreddit')
+df = df.withColumnRenamed('subreddit_2','subreddit')
+
+df = df.withColumnRenamed("created_utc","CreatedAt")
+df = df.withColumn("Month",f.month(f.col("CreatedAt")))
+df = df.withColumn("Year",f.year(f.col("CreatedAt")))
+df = df.withColumn("Day",f.dayofmonth(f.col("CreatedAt")))
+
+df = df.repartition('subreddit')
+df2 = df.sort(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
+df2 = df2.sortWithinPartitions(["subreddit","CreatedAt","link_id","parent_id","Year","Month","Day"],ascending=True)
+df2.write.parquet("/gscratch/comdata/output/reddit_comments_by_subreddit.parquet", mode='overwrite', compression='snappy')
+
+df = df.repartition('author')
+df3 = df.sort(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
+df3 = df3.sortWithinPartitions(["author","CreatedAt","subreddit","link_id","parent_id","Year","Month","Day"],ascending=True)
+df3.write.parquet("/gscratch/comdata/output/reddit_comments_by_author.parquet", mode='overwrite')
--- /dev/null
+from subprocess import Popen, PIPE
+import re
+from collections import defaultdict
+from os import path
+import glob
+
+def find_dumps(dumpdir, base_pattern):
+
+ files = glob.glob(path.join(dumpdir,base_pattern))
+
+ # build a dictionary of possible extensions for each dump
+ dumpext = defaultdict(list)
+ for fpath in files:
+ fname, ext = path.splitext(fpath)
+ dumpext[fname].append(ext)
+
+ ext_priority = ['.zst','.xz','.bz2']
+
+ for base, exts in dumpext.items():
+ found = False
+ if len(exts) == 1:
+ yield base + exts[0]
+ found = True
+ else:
+ for ext in ext_priority:
+ if ext in exts:
+ yield base + ext
+ found = True
+ assert(found == True)
+
+def open_fileset(files):
+ for fh in files:
+ print(fh)
+ lines = open_input_file(fh)
+ for line in lines:
+ yield line
+
+def open_input_file(input_filename):
+ if re.match(r'.*\.7z$', input_filename):
+ cmd = ["7za", "x", "-so", input_filename, '*']
+ elif re.match(r'.*\.gz$', input_filename):
+ cmd = ["zcat", input_filename]
+ elif re.match(r'.*\.bz2$', input_filename):
+ cmd = ["bzcat", "-dk", input_filename]
+ elif re.match(r'.*\.bz', input_filename):
+ cmd = ["bzcat", "-dk", input_filename]
+ elif re.match(r'.*\.xz', input_filename):
+ cmd = ["xzcat",'-dk', '-T 20',input_filename]
+ elif re.match(r'.*\.zst',input_filename):
+ cmd = ['zstd','-dck', input_filename]
+ try:
+ input_file = Popen(cmd, stdout=PIPE).stdout
+ except NameError as e:
+ print(e)
+ input_file = open(input_filename, 'r')
+ return input_file
+
#!/usr/bin/env bash
-# part2 should be run on one ore more spark nodes
+echo "!#/usr/bin/bash" > job_script.sh
+echo "source $(pwd)/../bin/activate" >> job_script.sh
+echo "python3 $(pwd)/submissions_2_parquet_part1.py" >> job_script.sh
-./submissions_2_parquet_part1.py
+srun -p comdata -A comdata --nodes=1 --mem=120G --time=48:00:00 job_script.sh
start_spark_and_run.sh 1 $(pwd)/submissions_2_parquet_part2.py
# 1. from gz to arrow parquet (this script)
# 2. from arrow parquet to spark parquet (submissions_2_parquet_part2.py)
-from collections import defaultdict
-from os import path
-import glob
import json
-import re
from datetime import datetime
-from subprocess import Popen, PIPE
-from multiprocessing import Pool, SimpleQueue
-
-dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/submissions"
-
-def find_json_files(dumpdir):
- base_pattern = "RS_20*.*"
-
- files = glob.glob(path.join(dumpdir,base_pattern))
-
- # build a dictionary of possible extensions for each dump
- dumpext = defaultdict(list)
- for fpath in files:
- fname, ext = path.splitext(fpath)
- dumpext[fname].append(ext)
-
- ext_priority = ['.zst','.xz','.bz2']
-
- for base, exts in dumpext.items():
- found = False
- if len(exts) == 1:
- yield base + exts[0]
- found = True
- else:
- for ext in ext_priority:
- if ext in exts:
- yield base + ext
- found = True
- assert(found == True)
-
-files = list(find_json_files(dumpdir))
-
-def read_file(fh):
- lines = open_input_file(fh)
- for line in lines:
- yield line
-
-def open_fileset(files):
- for fh in files:
- print(fh)
- lines = open_input_file(fh)
- for line in lines:
- yield line
-
-def open_input_file(input_filename):
- if re.match(r'.*\.7z$', input_filename):
- cmd = ["7za", "x", "-so", input_filename, '*']
- elif re.match(r'.*\.gz$', input_filename):
- cmd = ["zcat", input_filename]
- elif re.match(r'.*\.bz2$', input_filename):
- cmd = ["bzcat", "-dk", input_filename]
- elif re.match(r'.*\.bz', input_filename):
- cmd = ["bzcat", "-dk", input_filename]
- elif re.match(r'.*\.xz', input_filename):
- cmd = ["xzcat",'-dk', '-T 20',input_filename]
- elif re.match(r'.*\.zst',input_filename):
- cmd = ['zstd','-dck', input_filename]
- try:
- input_file = Popen(cmd, stdout=PIPE).stdout
- except NameError as e:
- print(e)
- input_file = open(input_filename, 'r')
- return input_file
+from multiprocessing import Pool
+from itertools import islice
+from helper import find_dumps, open_fileset
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
def parse_submission(post, names = None):
row.append(post[name])
return tuple(row)
+dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/submissions"
+
+files = list(find_dumps(dumpdir))
+
pool = Pool(28)
stream = open_fileset(files)
rows = pool.imap_unordered(parse_submission, stream, chunksize=int(N/28))
-from itertools import islice
-import pandas as pd
-import pyarrow as pa
-import pyarrow.parquet as pq
-
schema = pa.schema([
pa.field('id', pa.string(),nullable=True),
pa.field('author', pa.string(),nullable=True),
# spark script to make sorted, and partitioned parquet files
-import pyspark
from pyspark.sql import functions as f
-from pyspark.sql.types import *
-from pyspark import SparkConf, SparkContext
-from pyspark.sql import SparkSession, SQLContext
-import os
+from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
df = df.withColumn("subreddit_hash",f.sha2(f.col("subreddit"), 256)[0:3])
# next we gotta resort it all.
-df2 = df.sort(["subreddit","author","id","Year","Month","Day"],ascending=True)
+df = df.repartition("subreddit")
+df2 = df.sort(["subreddit","CreatedAt","id"],ascending=True)
+df2 = df.sortWithinPartitions(["subreddit","CreatedAt","id"],ascending=True)
df2.write.parquet("/gscratch/comdata/output/reddit_submissions_by_subreddit.parquet", partitionBy=["Year",'Month'], mode='overwrite')
# # we also want to have parquet files sorted by author then reddit.
-df3 = df.sort(["author","CreatedAt","subreddit","id","Year","Month","Day"],ascending=True)
+df = df.repartition("author")
+df3 = df.sort(["author","CreatedAt","id"],ascending=True)
+df3 = df.sortWithinPartitions(["author","CreatedAt","id"],ascending=True)
df3.write.parquet("/gscratch/comdata/output/reddit_submissions_by_author.parquet", partitionBy=["Year",'Month'], mode='overwrite')
os.remove("/gscratch/comdata/output/reddit_submissions.parquet_temp")
projects / cdsc_reddit.git / commitdiff