david cottrell created SPARK-20012: -------------------------------------- Summary: spark.read.csv schemas effectively ignore headers Key: SPARK-20012 URL: https://issues.apache.org/jira/browse/SPARK-20012 Project: Spark Issue Type: Bug Components: Input/Output Affects Versions: 2.1.0 Environment: pyspark Reporter: david cottrell Priority: Minor
New to Spark, so please direct me elsewhere if there is another place for this kind of discussion. To my understanding, schema are ordered *named* structures however it seems the names are not being used when reading files with headers. I had a quick look at the DataFrameReader code and it seems like it might not be too hard to a) let the schema set the "global" order of the columns b) per file, map the columns *by name* to the schema ordering and apply the types on load. A simple way of saying this is that the schema is an ordered dictionary and the files with headers only define dictionaries. A typical example showing what I think are the implications of this problem: {code} In [248]: a = spark.read.csv('./data/test.csv.gz', header=True, inferSchema=True).toPandas() In [249]: b = spark.read.csv('./data/0.csv.gz', header=True, inferSchema=True).toPandas() In [250]: d = pd.concat([a, b]) In [251]: df = spark.read.csv('./data/{test,0}.csv.gz', header=True, inferSchema=True).toPandas() In [252]: df[['b', 'c', 'd', 'e']] = df[['b', 'c', 'd', 'e']].astype(float) In [253]: a Out[253]: a b e d c 0 test -0.874197 0.168660 -0.948726 0.479723 1 test 1.124383 0.620870 0.159186 0.993676 2 test -1.429108 -0.048814 -0.057273 -1.331702 In [254]: b Out[254]: a b c d e 0 0 -1.671828 -1.259530 0.905029 0.487244 1 0 -0.024553 -1.750904 0.004466 1.978049 2 0 1.686806 0.175431 0.677609 -0.851670 In [255]: d Out[255]: a b c d e 0 test -0.874197 0.479723 -0.948726 0.168660 1 test 1.124383 0.993676 0.159186 0.620870 2 test -1.429108 -1.331702 -0.057273 -0.048814 0 0 -1.671828 -1.259530 0.905029 0.487244 1 0 -0.024553 -1.750904 0.004466 1.978049 2 0 1.686806 0.175431 0.677609 -0.851670 In [256]: df Out[256]: a b c d e 0 test -0.874197 0.168660 -0.948726 0.479723 1 test 1.124383 0.620870 0.159186 0.993676 2 test -1.429108 -0.048814 -0.057273 -1.331702 3 0 -1.671828 -1.259530 0.905029 0.487244 4 0 -0.024553 -1.750904 0.004466 1.978049 5 0 1.686806 0.175431 0.677609 -0.851670 {code} Example also posted here: http://stackoverflow.com/questions/42637497/pyspark-2-1-0-spark-read-csv-scrambles-columns -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org