So I had this PostgreSQL database that was getting a bit too big, and since it was really only for analytics, I figured it would be a good fit for putting in Hadoop+Hive instead.

(For those not completely familiar with this: Hadoop is sort of a job tracker and distributed file system. Hive is an SQL-like layer on top of that. I know the cool kids are now using Spark. Maybe for another day.)

The first thing you need to learn about the Hadoop ecosystem is its idiosyncratically fragmented structure. With PostgreSQL, you basically have the community website, the community mailing lists, the community source code distribution, the community binaries, and a handful of binaries made by Linux distributions. If you search the web for a problem with PostgreSQL, you will normally gets hits on one or more of: the documentation, the mailing lists, third-party mirrors of the mailing lists, or Stack Overflow. With Hadoop, you have the resources provided by the Apache Software Foundation, including the source distribution, bug tracker, documentation, and then bunch of commercial vendors with their parallel universes, including their own mutually incompatible binary distributions, their own copy of the documentation, their own mailing lists, their own bug trackers, etc. When you search for a problem with Hadoop, you will typically get hits from three separate copies of the documentation, about eight mailing lists, fifteen tutorials, and one thousand blog posts. And about 20 unanswered posts on Stack Overflow. Different vendors also favor different technology extensions. So if, say, you read that you should use some storage method, chances are it’s not even supported in a given distribution.

The next thing to know is that any information about Hadoop that is older than about two years is obsolete. Because they keep changing everything from command names to basic architecture. Don’t even bother reading old stuff. Don’t even bother reading anything.

So Hive. The basic setup is actually fairly well documented. You set up a Hadoop cluster, HDFS, create a few directories. Getting the permissions sorted out during these initial steps is not easy, but it seldom is. So you can create a few tables, load some data, run a few queries.

Nevermind that in its default configuration hive spits out about a dozen warnings on every startup about deprecated parameters and jar file conflicts. This is apparently well known. Look around in the internet for hive examples. They show the same output. Apparently the packaged versions of Hadoop and Hive are not tuned for each other.

Then you learn: In the default configuration, there can only be one Hive session connected at once. It doesn’t tell you this. Instead, when the second session wants to connect, it tells you

Exception in thread "main" java.lang.RuntimeException: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.metastore.HiveMetaStoreClient

followed by hundreds of lines of exception traces. This is Hive-speak for: “there is already one session connected”.

You see, Hive needs a, cough, cough, relational database to store its schema. By default, it uses embedded Derby, which allows only one connection at a time. If you want to connect more than one session at once, you need to set up an external “Hive metastore” on a MySQL or PostgreSQL database.

Nevermind that Derby can actually run in server mode. That’s apparently not supported by Hive.

So I had a PostgreSQL database handy and tried to set that up. I installed the PostgreSQL JDBC driver, created an external database, changed the Hive configuration to use an external database.

At this point, it turned out that the PostgreSQL JDBC driver was broken, so I had to downgrade to an older version. (The driver has since been fixed.)

After I got one that was working, Hive kept complaining that it couldn’t find a driver that matches the JDBC URL jdbc:postgresql://somehost/hive_metastore. The PostgreSQL JDBC driver explains in detail how to load the driver, but how do I get that into Hive?

The first suggestion from the internet was to add something like this to .hiverc:

add jar /usr/share/java/postgresql-jdbc.jar;

That doesn’t work. Remember, don’t believe anything you read on the internet.

In between I even tried download the MySQL JDBC driver (no, I don’t want to sign in with my Oracle account), but it had the same problem.

hive is actually a shell script which loads another shell script which loads a bunch of other shell scripts, which eventually starts java. After randomly poking around I determined that if I did

export HIVE_AUX_JARS_PATH=/usr/share/java/

it would pick up the jar files in that directory. OK, that worked.

Now I can create tables, load data, run simple queries, from more than one session. So I could do

SELECT * FROM mytable;

But as soon as I ran

SELECT count(*) FROM mytable;

it crapped out again: File does not exist: hdfs://namenode/usr/share/java/jline-0.9.94.jar

So it’s apparently looking for some jar file on HDFS rather than the regular file system. Some totally unrelated jar file, too.

The difference between the two queries is that the first one is answered by just dumping out data locally, whereas the second one generates a distributed map-reduce job. It doesn’t tell you that beforehand, of course. Or even afterwards.

After a while I figured that this must have something to do with the HIVE_AUX_JARS_PATH setting. I changed that to

export HIVE_AUX_JARS_PATH=/usr/share/java/postgresql-jdbc.jar;

so it would look at only one file, and sure enough it now complains File does not exist: hdfs://namenode/usr/share/java/postgresql-jdbc.jar

Apparently, the HIVE_AUX_JARS_PATH facility is for adding jars that contain user-defined functions that you need at run time. As far as I can tell, there is no separate setting for adding jars that you only need locally.

There are workarounds for that on the internet, of varying bizarreness, none of which worked. Remember, don’t believe anything you read on the internet.

In the end, I indulged it and just uploaded that jar file into HDFS. Whatever.

I then put my data loading job into cron, which quickly crapped out because JAVA_HOME is not set in the cron environment. After that was fixed, I let my data loading jobs run for a while.

Later, I wanted clear out the previous experiments, drop all tables, and start again. Apparently, dropping a table in Hive takes a very long time. Actually, no. When you use PostgreSQL for the Hive metastore, any attempt to drop a table will hang indefinitely.

Someone summarized the issue:

You are the first person I have heard of using postgres. I commend you for not succumbing to the social pressure and just installing mysql. However I would advice succumbing to the social pressure and using either derby or mysql.

The reason I say this is because jpox “has support” for a number of data stores (M$ SQL server) however, people have run into issues with them. Databases other then derby and mysql ‘should work’ but are generally untested.

Not that actually testing it would take much work. It’s not like Hive doesn’t have any tests. Just add some tests.

It’s funny that they didn’t write “You are the first person I have heard of using hive”. Clearly, nobody has ever actually used this.

Anyway, somehow I ended up creating the metastore schema manually by copying and pasting various pieces from the internet and raw files. Shudder.

How about more fun? Here is a run-of-the-mill SQL parse error:

NoViableAltException(26@[221:1: constant : ( Number | dateLiteral | StringLiteral | stringLiteralSequence | BigintLiteral | SmallintLiteral | TinyintLiteral | DecimalLiteral | charSetStringLiteral | booleanValue );])
	at org.antlr.runtime.DFA.noViableAlt(
	at org.antlr.runtime.DFA.predict(
	at org.apache.hadoop.hive.ql.parse.HiveParser_IdentifiersParser.constant(
	at org.apache.hadoop.hive.ql.parse.HiveParser_IdentifiersParser.partitionVal(
	at org.apache.hadoop.hive.ql.parse.HiveParser_IdentifiersParser.partitionSpec(
	at org.apache.hadoop.hive.ql.parse.HiveParser_IdentifiersParser.tableOrPartition(
	at org.apache.hadoop.hive.ql.parse.HiveParser.tableOrPartition(
	at org.apache.hadoop.hive.ql.parse.HiveParser.insertClause(
	at org.apache.hadoop.hive.ql.parse.HiveParser.regular_body(
	at org.apache.hadoop.hive.ql.parse.HiveParser.queryStatement(
	at org.apache.hadoop.hive.ql.parse.HiveParser.queryStatementExpression(
	at org.apache.hadoop.hive.ql.parse.HiveParser.execStatement(
	at org.apache.hadoop.hive.ql.parse.HiveParser.statement(
	at org.apache.hadoop.hive.ql.parse.ParseDriver.parse(
	at org.apache.hadoop.hive.ql.Driver.compile(
	at org.apache.hadoop.hive.ql.Driver.compile(
	at org.apache.hadoop.hive.ql.Driver.runInternal(
	at org.apache.hadoop.hive.cli.CliDriver.processLocalCmd(
	at org.apache.hadoop.hive.cli.CliDriver.processCmd(
	at org.apache.hadoop.hive.cli.CliDriver.processLine(
	at org.apache.hadoop.hive.cli.CliDriver.processLine(
	at org.apache.hadoop.hive.cli.CliDriver.processReader(
	at org.apache.hadoop.hive.cli.CliDriver.processFile(
	at org.apache.hadoop.hive.cli.CliDriver.executeDriver(
	at org.apache.hadoop.hive.cli.CliDriver.main(
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(
	at java.lang.reflect.Method.invoke(
	at org.apache.hadoop.util.RunJar.main(
FAILED: ParseException line 4:20 cannot recognize input near 'year' '(' 'event_timestamp' in constant

In PostgreSQL, this might say

syntax error at or near "("

with a pointer to the actual query.

I just put a function call somewhere where it didn’t belong. The documentation is very terse and confusing about a lot of these things. And the documentation is kept as a series of wiki pages.

So now I have a really slow distributed version of my PostgreSQL database, which stores its schema in another PostgreSQL database. I forgot why I needed that.