Really intensive database operations including datasest collections
but other things too like multi-running tools or workflows over many
individual datasets for instance - can very easily overwhelm the
default sqlite database. This is frustrating and shouldn't happen -
but it does unfortunately. I would recommend using a postgres database
when testing out dataset collections. The good news is that it is
easier than ever to get a fully fledged production-quality server
thanks to Bjoern's docker server
(https://github.com/bgruening/docker-galaxy-stable) - it comes bundled
with Postgres and Slurm so it should be able to handle the collection
operations. If you need to run Galaxy on a non-containerized server
(for instance because that is where the software is) more information
on setting up Galaxy can be found here
https://wiki.galaxyproject.org/Admin/Config/Performance/ProductionServer.
Here is a Trello card to track progress on the database optimization
efforts if you are interested https://trello.com/c/UPLsMKQI.
Very sorry.
On Mon, Jan 26, 2015 at 9:35 AM, Torsten Houwaart
<houwaart@informatik.uni-freiburg.de> wrote:
Hello Galaxy Devs,
I was using data collections (for the first time) for a new workflow of
ours
and I ran into this problem. There was no complaint by the
workflow-editor
and I could start the workflow but then <see below> happened.
If you need more information about the workflow or otherwise let me know.
Best,
Torsten H.
job traceback:
Traceback (most recent call last):
File
"/usr/local/galaxy/galaxy-dist/lib/galaxy/jobs/runners/__init__.py",
line 565, in finish_job
job_state.job_wrapper.finish( stdout, stderr, exit_code )
File "/usr/local/galaxy/galaxy-dist/lib/galaxy/jobs/__init__.py", line
1250, in finish
self.sa_session.flush()
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/orm/scoping.py",
line 114, in do
return getattr(self.registry(), name)(*args, **kwargs)
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/orm/session.py",
line 1718, in flush
self._flush(objects)
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/orm/session.py",
line 1789, in _flush
flush_context.execute()
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/orm/unitofwork.py",
line 331, in execute
rec.execute(self)
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/orm/unitofwork.py",
line 475, in execute
uow
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/orm/persistence.py",
line 59, in save_obj
mapper, table, update)
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/orm/persistence.py",
line 485, in _emit_update_statements
execute(statement, params)
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/engine/base.py",
line 1449, in execute
params)
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/engine/base.py",
line 1584, in _execute_clauseelement
compiled_sql, distilled_params
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/engine/base.py",
line 1698, in _execute_context
context)
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/engine/base.py",
line 1691, in _execute_context
context)
File
"/usr/local/galaxy/galaxy-dist/eggs/SQLAlchemy-0.7.9-py2.7-linux-x86_64-ucs2.egg/sqlalchemy/engine/default.py",
line 331, in do_execute
cursor.execute(statement, parameters)
DBAPIError: (TransactionRollbackError) deadlock detected
DETAIL: Process 3144 waits for ShareLock on transaction 2517124; blocked
by
process 3143.
Process 3143 waits for ShareLock on transaction 2517123; blocked by
process
3144.
HINT: See server log for query details.
'UPDATE workflow_invocation SET update_time=%(update_time)s WHERE
workflow_invocation.id = %(workflow_invocation_id)s' {'update_time':
datetime.datetime(2015, 1, 26, 14, 20, 4, 155440),
'workflow_invocation_id':
5454}
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