Antonio Piccolboni created SPARK-10804:
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Summary: "LOCAL" in LOAD DATA LOCAL INPATH means "remote"
Key: SPARK-10804
URL: https://issues.apache.org/jira/browse/SPARK-10804
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 1.5.0
Reporter: Antonio Piccolboni
Connecting with a remote thriftserver with a custom JDBC client or beeline,
load data local inpath fails. Hiveserver2 docs explain in a quick comment that
local now means local to the server. I think this is just a rationalization for
a bug. When a user types "local"
# it needs to be local to him, not some server
# Failing 1., one needs to have a way to determine what local means and create
a "local" item under the new definition.
With the thirftserver, I have a host to connect to, but I don't have any way to
create a file local to that host, at least in spark. It may not be desirable to
create user directories on the thriftserver host or running file transfer
services like scp. Moreover, it appears that this syntax is unique to Hive and
Spark but its origin can be traced to LOAD DATA LOCAL INFILE in Oracle and was
adopted by mysql. In the latter docs we can read "If LOCAL is specified, the
file is read by the client program on the client host and sent to the server.
The file can be given as a full path name to specify its exact location. If
given as a relative path name, the name is interpreted relative to the
directory in which the client program was started". This is not to say that the
spark or hive teams are bound to what Oracle and Mysql do, but to support the
idea that the meaning of LOCAL is settled. For instance, the Impala
documentation says: "Currently, the Impala LOAD DATA statement only imports
files from HDFS, not from the local filesystem. It does not support the LOCAL
keyword of the Hive LOAD DATA statement." I think this is a better solution.
The way things are in thriftserver, I developed a client under the assumption
that I could use LOAD DATA LOCAL INPATH and all tests where passing in
standalone mode, only to find with the first distributed test that
# LOCAL means "local to server", a.k.a. "remote"
# INSERT INTO ... VALUES is not supported
# There is really no workaround unless one assumes access what data store spark
is running against , like HDFS, and that the user can upload data to it.
In the space of workarounds it is not terrible, but if you are trying to write
a self-contained spark package, that's a defeat and makes writing tests
particularly hard.
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