This is done in preparation for the proposal API.
In order to compute proposals we need to consume the
API of the DataStore, but the code does not need to
be in the DataStore.
Extracting the API allows us to separate these concerns.
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.
Date increments have usually higher values.
I had hoped to reduce the file size by a lot. But in my example data
with 44 million entries (real life data) it only reduced the storage
size by 1.5%.
Also fixed a bug in PdbReader that prevented other values for the
CONTINUATION byte.
Also added a small testing tool that prints the content of a pdb file.
It is not (yet) made available as standalone tool, but during
debugging sessions it is very useful.
Before we could only group by a single field. But it is acutally
very useful to group by multiple fields. For example to see the
graph for a small set of methods grouped by host and project.
If for 10 seconds no new entry is received, then all open
files are flushed and closed.
We do this to make sure, that we do not loose data, when
we kill the process.
There is still a risk of data loss if we kill the process
while entries are received.
