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.
In the previous changeset the code that determined
which axis the plots used was implemented as a
side effect of getting the Gnuplot definition of
an axis.
Changed that to an explit update call with simpler
logic.
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.
Guava's cache does not evict elements reliably by
time. Configure a cache to have a lifetime of n
seconds, then you cannot expect that an element is
actually evicted after n seconds with Guava.
The old implementation searched for all possible values and then
executed each query to see what matches.
The new implementation uses several indices to find only
the matching values.
The reason seems to be the number of memory allocations. In order
to create the union of 100 lists we have 99 memory allocations.
The first needs the space for the first two lists, the second the
space for the first three lists, and so on.
We can reduce the number of allocations drastically (in many
cases to one) by leveraging the fact that many of the lists
were already sorted, non-overlapping and increasing, so that
we can simply concatenate them.
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.
In the last commit I added a lastAccessMap to the HotEntryCache.
This map made it much more efficient to evict entries. But it
also made and put and get operation much more expensive. Overall
that change lead to a 65% decrease in ingestion performance of
the PerformanceDB.
Fixed by removing the map again. Eviction has to look at all
elements again.
Instead of spawning a new thread for every cache, we use a single thread
that will evict entries from all caches.
The thread keeps a weak reference to the caches, so that they can be
garbage collected.
Replaces the use of in-memory data structures with the PersistentMap.
This is the crucial step in reducing memory usage for both persistent
storage and main memory.
Before the offset of the root node was hard-coded.
Now the offset of the pointer to the root node is hard-coded.
That allows us to replace the root node.
LuDB has a few disadvantages.
1. Most notably disk space. H2 wastes a lot of valuable disk space.
For my test data set with 44 million entries it is 14 MB
(sometimes a lot more; depends on H2 internal cleanup). With
data-store it is 15 KB.
Overall I could reduce the disk space from 231 MB to 200 MB (13.4 %
in this example). That is an average of 4.6 bytes per entry.
2. Speed:
a) Liquibase is slow. The first time it takes approx. three seconds
b) Query and insertion. with data-store we can insert entries
up to 1.6 times faster.
Data-store uses a few tricks to save disk space:
1. We encode the tags into the file names.
2. To keep them short we translate the key/value of the tag into
shorter numbers. For example "foo" -> 12 and "bar" to 47. So the
tag "foo"/"bar" would be 12/47.
We then translate this number into a numeral system of base 62
(a-zA-Z0-9), so it can be used for file names and it is shorter.
That way we only have to store the mapping of string to int.
3. We do that in a simple tab separated file.
