I hereby claim:
- I am vertexclique on github.
- I am vertexclique (https://keybase.io/vertexclique) on keybase.
- I have a public key whose fingerprint is 334E 336D CEE3 1C3A 6E61 0B54 D20F 2F5E 6DFD 6F11
To claim this, I am signing this object:
exact_buf_write/4.00 KiB | |
time: [121.39 ns 122.07 ns 122.79 ns] | |
thrpt: [31.067 GiB/s 31.251 GiB/s 31.424 GiB/s] | |
change: | |
time: [-1.6055% -1.0408% -0.4293%] (p = 0.00 < 0.05) | |
thrpt: [+0.4312% +1.0517% +1.6317%] | |
Change within noise threshold. | |
Found 4 outliers among 100 measurements (4.00%) | |
4 (4.00%) high mild | |
exact_buf_write/256 KiB time: [71.903 ns 72.184 ns 72.523 ns] |
use lever::index::zonemap::ZoneMap; | |
fn main() { | |
let customers: Vec<i32> = vec![ | |
vec![1, 0, -1, -2].repeat(2), | |
vec![1, 2, 3, 4].repeat(3) | |
].concat(); | |
let products = vec![4, 3, 2, 1].repeat(100); | |
let payouts = vec![4, 2, 6, 7].repeat(100); |
@article{Ziauddin2017, | |
abstract = {In recent years, the data warehouse industry has witnessed decreased use of indexing but increased use of compression and clustering of data facilitating efficient data access and data pruning in the query processing area. A classic example of data pruning is the partition pruning, which is used when table data is range or list partitioned. But lately, techniques have been developed to prune data at a lower granularity than a table partition or sub-partition. A good example is the use of data pruning structure called zone map. A zone map prunes zones of data from a table on which it is defined. Data pruning via zone map is very effective when the table data is clustered by the filtering columns. The database industry has offered support to cluster data in tables by its local columns, and to define zone maps on clustering columns of such tables. This has helped improve the performance of queries that contain filter predicates on local columns. However, queries in data warehou |
use lever::index::zonemap::ZoneMap; | |
fn main() { | |
let customers: Vec<i32> = vec![ | |
vec![1, 0, -1, -2].repeat(500), | |
vec![1, 2, 3, 4].repeat(250) | |
].concat(); | |
let ingestion_data = vec![ | |
("customers", customers.as_slice()), |
test result: ok. 0 passed; 0 failed; 501 ignored; 0 measured; 0 filtered out | |
Running /home/vertexclique/projects/arrow/rust/target/release/deps/aggregate_kernels-6d55c08b7328dd4e | |
sum 512 time: [631.40 ns 631.72 ns 632.14 ns] | |
change: [+0.0700% +0.1267% +0.1817%] (p = 0.00 < 0.05) | |
Change within noise threshold. | |
Found 20 outliers among 100 measurements (20.00%) | |
1 (1.00%) low mild | |
19 (19.00%) high severe |
=========== RwLock ============= | |
Jun 07 12:06:40.728 INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=1}: bustle: generating operation mix | |
Jun 07 12:06:40.729 INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=1}: bustle: generating key space | |
Jun 07 12:06:40.912 INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=1}: bustle: constructing initial table | |
Jun 07 12:06:40.941 INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=1}: bustle: start workload mix | |
Jun 07 12:06:45.524 INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=1}: bustle: workload mix finished took=4.582992212s ops=25165824 avg=181ns | |
25165824 operations across 1 thread(s) in 4.582992212s; time/op = 181ns | |
Jun 07 12:06:45.600 INFO benchmark{mix=Mix { read: 94, insert: 2, remove: 1, update: 3, upsert: 0 } threads=2}: bustle: generating operation mix | |
Jun 07 12:06: |
KORQ_VERSION=0.4.0 | |
cargo install korq | |
#OR | |
curl -SL https://github.com/vertexclique/korq/releases/download/$KORQ_VERSION/korq-$KORQ_VERSION-x86_64-apple-darwin.tar.gz | \ | |
tar xzv && mv korq /usr/local/bin && chmod 777 /usr/local/bin/korq |
I hereby claim:
To claim this, I am signing this object:
I hereby claim:
To claim this, I am signing this object: