Add code for running tf-idf at the weekly level.

[cdsc_reddit.git] / comments_2_parquet_part1.py

diff --git a/comments_2_parquet_part1.py b/comments_2_parquet_part1.py
index faea040402a30f66c147dd034ed70ed7e4ac1e86..d3c7b7cfef3d2d127fd92b5faef98f616210a11a 100755 (executable)
--- a/comments_2_parquet_part1.py
+++ b/comments_2_parquet_part1.py
@@ -8,8 +8,6 @@ import pandas as pd
 import pyarrow as pa
 import pyarrow.parquet as pq
 
 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"]
 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"]


@@ -48,15 +46,15 @@ def parse_comment(comment, names= None):
 
 #    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')])
 
 
 #    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"
+dumpdir = "/gscratch/comdata/raw_data/reddit_dumps/comments/"

 
 

-files = list(find_dumps(dumpdir, base_pattern="RC_20*.*"))
+files = list(find_dumps(dumpdir, base_pattern="RC_20*"))

 
 pool = Pool(28)
 
 stream = open_fileset(files)
 
 
 pool = Pool(28)
 
 stream = open_fileset(files)
 

-N = 100000
+N = int(1e4)

 
 rows = pool.imap_unordered(parse_comment, stream, chunksize=int(N/28))
 
 
 rows = pool.imap_unordered(parse_comment, stream, chunksize=int(N/28))
 


@@ -80,13 +78,38 @@ schema = pa.schema([
     pa.field('error', 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)
+from pathlib import Path
+p = Path("/gscratch/comdata/output/reddit_comments.parquet_temp2")
+
+if not p.is_dir():
+    if p.exists():
+        p.unlink()
+    p.mkdir()
+
+else:
+    list(map(Path.unlink,p.glob('*')))
+
+part_size = int(1e7)
+part = 1
+n_output = 0
+writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
+
+while True:
+    if n_output > part_size:
+        if part > 1:
+            writer.close()
+
+        part = part + 1
+        n_output = 0
+    
+        writer = pq.ParquetWriter(f"/gscratch/comdata/output/reddit_comments.parquet_temp2/part_{part}.parquet",schema=schema,compression='snappy',flavor='spark')
+
+    n_output += N
+    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()