Commit Graph

11 Commits

Author SHA1 Message Date
b01d267300 update primitiveCollections
The new version of primitiveCollections requires Java 10.
2018-08-18 08:32:27 +02:00
99dbf31d8a update 3rd party libs 2018-08-09 07:20:09 +02:00
daaa0e6907 update dependencies
gradle to 4.8
jackson to 2.9.6
spring-boot to 2.0.3
guava to 25.1-jre
gradle-versions-plugin to 0.19.0
2018-06-17 08:59:48 +02:00
82b8a8a932 reduce memory footprint by lazily intializing the path in Doc
The path in Doc is not optional. This reduces memory consumption,
because we only have to store a long (the offset in the listing file).
This assumes, that only a small percentage of Docs is requested.
2018-05-06 12:58:10 +02:00
b06ccb0d00 update 3rd party libs
spring boot to 2.0.1
guava to 24.1-jre
jackson to 2.9.5
log4j2 to 2.10.0 (same version as pulled by spring boot)
testng to 6.14.3
2018-04-21 20:01:39 +02:00
b439c9d79a update third-party libs
antlr4: 4.7 -> 4.7.1
commons-lang3: 3.6 -> 3.7
2018-01-21 08:44:30 +01:00
ahr
2df66c7b2f update primitiveCollections
This fixes a performance issue where the IntLists were not sorted and
therefore slow union/intersection algorithms were chosen.
2017-12-29 08:20:52 +01:00
ahr
8225dd2077 update primitiveCollections to 0.1.20171216143737
Use intersection and union methods from IntList.
2017-12-16 17:35:16 +01:00
a636f2b9bd update primitive collections to 0.1.20171007100354 2017-11-18 10:09:47 +01:00
347f1fdc74 update 3rd-party libraries 2017-09-23 18:24:51 +02:00
ac1ee20046 replace ludb with data-store
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.
2017-04-16 09:07:28 +02:00