This code manages the database connections, setting their isolation level correctly
and ensuring the idempotent schema is applied before they're used.
Applying the schema on startup means we don't need to deal with the database's state,
setting it up before running, running migrations etc. However, it does put constraints on
the changes we can safely make.
Our use of seralizable isolation means that all transactions can be treated as fully
independent - the server must behave as though they'd been run seperately in some valid order.
This will give us the least surprising results when multiple connections try to modify the same
data, though we'll need to deal with occasional transaction commit failures due to conficts.
get_best_segments can sometimes take a very long time,
we don't want to stop other work from happening while it's ongoing.
So we ask gevent to run other things until there's no other work to do,
then we do one hour, then check back with gevent again.
In combination with the performance improvements, this should mean we don't block
other things from running for more than a few hundred ms at most.
strptime is much faster but can't handle as varied formats.
But in this case we fully control the format, so there's no reason not to use it.
Profiling suggests we spend about 80% of our time in get_best_segments just parsing dates,
so this is a signifigant performance gain.
The prometheus client uses a threading.Lock() to prevent shared access to
certain metric state. This lock is taken as part of doing collection, as well
as during metric.labels().
We hit a deadlock where our stack sampler signal arrived during a collection,
when the lock was held. This meant that flamegraph.labels() blocked forever,
and the lock was never released, hanging all metrics collection.
Our solution is a hack, which is to reach into the internals of our metric object
and replace its lock with a dummy one. This is reasonably safe, but only as long as
the prometheus_client internal structure doesn't change signfigiantly.
The function is quite customizable and therefore quite complex, but it allows us to
easily annotate a function to be timed with labels based on input and output,
as well as normalize results based on amount of work done to get a better
picture of the actual amount of time taken per unit of work.
This will help us monitor for performance issues.
I ran `pyflakes` on the repo and found these bugs:
```
./common/common.py:289: undefined name 'random'
./downloader/downloader/main.py:7: 'random' imported but unused
./backfiller/backfiller/main.py:150: undefined name 'variant'
./backfiller/backfiller/main.py:158: undefined name 'timedelta'
./backfiller/backfiller/main.py:171: undefined name 'sort'
./backfiller/backfiller/main.py:173: undefined name 'sort'
```
(ok, the "imported but unused" one isn't a bug, but the rest are)
This fixes those, as well as a further issue I saw with sorting of hours.
Iterables are not sortable. As an obvious example, what if your iterable was infinite?
As a result, any attempt to sort an iterable that is not already a friendly type like a list
or tuple will result in an error. We avoid this by coercing to list, fully realising the iterable
and putting it into a form that python will let us sort. It also avoids the nasty side-effect
of mutating the list that gets passed into us, which the caller may not expect. Consider this example:
```
>>> my_hours = ["one", "two", "three"]
>>> print my_hours
["one", "two", "three"]
>>> backfill_node(base_dir, node, stream, variants, hours=my_hours, order='forward')
>>> print my_hours
["one", "three", "two"]
```
Also, one of the linter errors was non-trivial to fix - we were trying to get a list of hours
(which is an api call for a particular variant), but at a time when we weren't dealing with a single
variant. My solution was to get a list of hours for ALL variants, and take the union.
This is needed by both the restreamer and the cutter, hence its inclusion in common.
The algorithm is pretty simple - it takes the 'best' segment per start time by full first,
then length of partial. All the other complexity is mainly just around detecting and reporting holes,
and being inclusive of start/end points.
Mike Lang
Christopher Usher
Christopher Usher