DB engine comparison
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c84dc6bd35
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10 changed files with 84 additions and 4 deletions
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@ -10,7 +10,7 @@ ggplot(s, aes(x=total_objects, y=batch_dur_sec)) +
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y="Time (in sec) spent sending a batch (8192 objects)",
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y="Time (in sec) spent sending a batch (8192 objects)",
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x="Total number of objects stored in the cluster",
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x="Total number of objects stored in the cluster",
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caption="Get the code to reproduce this graph at https://git.deuxfleurs.fr/Deuxfleurs/mknet",
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caption="Get the code to reproduce this graph at https://git.deuxfleurs.fr/Deuxfleurs/mknet",
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title="Storing 1M+ files on Garage! Impact of existing data on cluster interactiveness",
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title="Storing 1M+ files in a bucket! Impact of existing data on cluster interactiveness",
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subtitle="Daemon: Garage v0.8 beta 2 with LMDB as db_engine\nBenchmark: 128 batch. 8192 objects/batch. 32 threads/batch. 256 objects/thread. 16-byte/objects.\nEnvironment: mknet (Ryzen 5 1400, 16GB RAM, SSD). DC topo (3 nodes, 1Gb/s, 1ms latency).") +
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subtitle="Daemon: Garage v0.8 beta 2 with LMDB as db_engine\nBenchmark: 128 batch. 8192 objects/batch. 32 threads/batch. 256 objects/thread. 16-byte/objects.\nEnvironment: mknet (Ryzen 5 1400, 16GB RAM, SSD). DC topo (3 nodes, 1Gb/s, 1ms latency).") +
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theme_classic()
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theme_classic()
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ggsave("./plot.png", width=200, height=120, units="mm")
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ggsave("./plot.png", width=200, height=120, units="mm")
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artifacts/2022-09-24-warp-db-engine/lmdb.log
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artifacts/2022-09-24-warp-db-engine/lmdb.log
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Mixed operations.
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Operation: DELETE, 10%, Concurrency: 20, Ran 4m59s.
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* Throughput: 266.79 obj/s
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Operation: GET, 45%, Concurrency: 20, Ran 4m59s.
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* Throughput: 0.29 MiB/s, 1200.51 obj/s
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Operation: PUT, 15%, Concurrency: 20, Ran 4m59s.
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* Throughput: 0.10 MiB/s, 400.20 obj/s
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Operation: STAT, 30%, Concurrency: 20, Ran 4m59s.
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* Throughput: 800.32 obj/s
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Cluster Total: 0.39 MiB/s, 2667.83 obj/s over 5m0s.
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Aggregated data saved to /tmp/mknet-store/warp.csv
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artifacts/2022-09-24-warp-db-engine/plot.R
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artifacts/2022-09-24-warp-db-engine/plot.R
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library(tidyverse)
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read_csv("summary.csv")-> s
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ggplot(s, aes(x=db_engine,y=rate)) +
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scale_y_continuous(expand=c(0,0), breaks = scales::pretty_breaks(n = 10))+
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geom_bar(stat='identity',fill="#00BFC4", color="black") +
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coord_flip() +
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facet_wrap(~operation, scales = "free", dir="v") +
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labs(
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x="Metadata Engine",
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y="Object/sec",
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caption="Get the code to reproduce this graph at https://git.deuxfleurs.fr/Deuxfleurs/mknet",
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title="Comparison of Garage's metadata engines with \"minio/warp\"",
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subtitle="Daemon: Garage v0.8 no-fsync to avoid being impacted by block manager\nBenchmark: warp, mixed mode, 5min bench, 256B objects, initialized with 200 objects.\nEnvironment: mknet (Ryzen 5 1400, 16GB RAM, SSD). DC topo (3 nodes, 1Gb/s, 1ms latency).",
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) +
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theme_classic()
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ggsave("./plot.png", width=200, height=120, units="mm")
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artifacts/2022-09-24-warp-db-engine/plot.png
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artifacts/2022-09-24-warp-db-engine/plot.png
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artifacts/2022-09-24-warp-db-engine/sled.log
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artifacts/2022-09-24-warp-db-engine/sled.log
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Mixed operations.
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Operation: DELETE, 10%, Concurrency: 20, Ran 3m49s.
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* Throughput: 129.83 obj/s
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Operation: GET, 45%, Concurrency: 20, Ran 3m49s.
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* Throughput: 0.14 MiB/s, 584.32 obj/s
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Operation: PUT, 15%, Concurrency: 20, Ran 3m49s.
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* Throughput: 0.05 MiB/s, 194.81 obj/s
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Operation: STAT, 30%, Concurrency: 20, Ran 3m49s.
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* Throughput: 389.52 obj/s
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Cluster Total: 0.19 MiB/s, 1298.49 obj/s over 3m49s.
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Aggregated data saved to /tmp/mknet-store/warp.csv
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artifacts/2022-09-24-warp-db-engine/sqlite.log
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artifacts/2022-09-24-warp-db-engine/sqlite.log
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Mixed operations.
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Operation: DELETE, 10%, Concurrency: 20, Ran 4m55s.
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* Throughput: 1.29 obj/s
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Operation: GET, 45%, Concurrency: 20, Ran 4m55s.
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* Throughput: 0.00 MiB/s, 5.75 obj/s
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Operation: PUT, 15%, Concurrency: 20, Ran 4m55s.
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* Throughput: 0.00 MiB/s, 1.93 obj/s
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Operation: STAT, 30%, Concurrency: 20, Ran 4m56s.
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* Throughput: 3.86 obj/s
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Cluster Total: 0.00 MiB/s, 12.83 obj/s over 4m55s.
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Aggregated data saved to /tmp/mknet-store/warp.csv
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artifacts/2022-09-24-warp-db-engine/summary.csv
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artifacts/2022-09-24-warp-db-engine/summary.csv
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db_engine,operation,rate
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lmdb,delete,266.79
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lmdb,get,1200.51
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lmdb,put,400
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lmdb,stat,800
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sled,delete,129.83
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sled,get,584.32
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sled,put,194.81
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sled,stat,389.52
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sqlite,delete,1.29
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sqlite,get,5.75
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sqlite,put,1.93
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sqlite,stat,3.86
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@ -41,6 +41,7 @@ garage = grg_path({
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warp = {
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warp = {
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"warp-fast": "mixed --obj.size 5M --objects 200 --duration=5m",
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"warp-fast": "mixed --obj.size 5M --objects 200 --duration=5m",
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"warp-small-obj": "mixed --obj.size 256 --objects 200 --duration=5m",
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"warp-default": "mixed"
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"warp-default": "mixed"
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}
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}
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@ -2,19 +2,23 @@
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from fragments import garage, warp, shared, flavor
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from fragments import garage, warp, shared, flavor
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import sys
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import sys
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db_engines = [ 'sled', 'lmdb', 'sqlite' ]
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conf_root = {}
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for fl in sys.argv[1:]:
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for fl in sys.argv[1:]:
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if fl in flavor.garage:
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if fl in flavor.garage:
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garage.version = flavor.garage[fl]
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garage.version = flavor.garage[fl]
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if fl in flavor.warp:
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elif fl in flavor.warp:
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warp.bench = flavor.warp[fl]
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warp.bench = flavor.warp[fl]
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elif fl in db_engines:
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conf_root['db_engine'] = fl
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if shared.id() == 1:
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if shared.id() == 1:
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garage.deploy_coord()
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garage.deploy_coord(uroot=conf_root)
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warp.on_garage()
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warp.on_garage()
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garage.delete_key()
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garage.delete_key()
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garage.destroy()
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garage.destroy()
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else:
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else:
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garage.deploy_follow()
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garage.deploy_follow(uroot=conf_root)
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garage.sync_on_key_down()
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garage.sync_on_key_down()
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garage.destroy()
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garage.destroy()
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shared.log("bye")
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shared.log("bye")
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