To make it easier/possible to write stable unit test the CSV upload
can optionally wait until all entries have been flushed to disk.
This is necessary for tests that ingest data and then read the data.
We want to be able to use @SpringBootTest tests that fully initialize
the Spring application. This is much easier done with Junit than TestNG.
Gradle does not support (at least not easily) to run Junit and TestNG
tests. Therefore we switch to Junit with all tests.
The original reason for using TestNG was that Junit didn't support
data providers. But that finally changed in Junit5 with
ParameterizedTest.
We had a method that returned the values of a field
with respect to a query. That method was inefficient,
because it executed the query, fetched all Docs
and collected the values.
The autocomplete method we introduced a while back
can answer the same question but much more efficiently.
In order to prevent files from getting too big and
make it easier to implement retention policies, we
are splitting all files into chunks. Each chunk
contains the data for a time interval (1 month per
default).
This first changeset introduces the ClusteredPersistentMap
that implements this for PersistentMap. It is used
for a couple (not all) of indices.
Due to a mistake in Tag which added all strings used
by Tag into the String dictionary, the dictionary
did contain all values that were used in queries.
The CacheKey is used as a key in a HashMap. Lookup can
be faster if the CacheKey is comparable when there are
hash collisions.
In this case I was not able to measure any effect. I am
keeping the comparables nonetheless, because the can
only have a positive effect.
Calling Instant.now() several hundred thousand times per
second can be expensive. In my measurements >10% of the
time spend when loading new data was spend calling
Instant.now().
Fixed this by storing an Instant as static member and
updating it periodically in a separate thread.
One bottleneck was the blocking queue used to transport entries
from the listener thread to the ingestor thread.
Reduced the bottleneck by batching entries.
Interestingly the batch size of 100 was better than batch size
of 1000 and better than 10.
Compared to FastISODateParser.parse, which returns an
OffsetDateTime object, parseAsEpochMilli returns the
epoch time millis. The performance improvement for
date parsing alone is roughly 100% (8m dates/s to
18m dates/s).
Insertion speed improved from 13-14s for 1.6m entries
to 11.5-12.5s.
The cache must remove/evict writers after a few seconds, but EhCache
only evicts entries when a new entry is added. That is not acceptable
for us, because that would leave lots of files open and we would need
a second mechanism to close them.
Therefore I write a simple wrapper for a ConcurrentHashMap that evicts
entries after timeToLive+5s.
- PdbFiles no longer require dates to be monotonically
increasing. Therefore TagsToFile does not have to ensure
this. => We only have one file per Tags.
- Use EhCache instead of HashMap.
