mirror of
https://github.com/trustgraph-ai/trustgraph.git
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bffaf62490
* Separate memgraph query/write modules to optimise for memgraph * Used 1GB memory for Memgraph * Deployed specialised memgraph query/write processors, created memgraph indexes * One triple is loaded as a single transaction * Fixed index creation
68 lines
1.9 KiB
Jsonnet
68 lines
1.9 KiB
Jsonnet
local base = import "base/base.jsonnet";
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local images = import "values/images.jsonnet";
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{
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"memgraph" +: {
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create:: function(engine)
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local container =
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engine.container("memgraph")
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.with_image(images.memgraph_mage)
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.with_environment({
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MEMGRAPH: "--storage-properties-on-edges=true --storage-enable-edges-metadata=true"
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})
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.with_limits("1.0", "1000M")
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.with_reservations("0.5", "1000M")
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.with_port(7474, 7474, "api")
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.with_port(7687, 7687, "api2");
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local containerSet = engine.containers(
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"memgraph", [ container ]
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);
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local service =
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engine.service(containerSet)
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.with_port(7474, 7474, "api")
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.with_port(7687, 7687, "api2");
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engine.resources([
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containerSet,
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service,
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])
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},
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"memgraph-lab" +: {
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create:: function(engine)
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local container =
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engine.container("lab")
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.with_image(images.memgraph_lab)
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.with_environment({
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QUICK_CONNECT_MG_HOST: "memgraph",
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QUICK_CONNECT_MG_PORT: "7687",
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})
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.with_limits("1.0", "512M")
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.with_reservations("0.5", "512M")
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.with_port(3010, 3000, "http");
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local containerSet = engine.containers(
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"lab", [ container ]
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);
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local service =
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engine.service(containerSet)
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.with_port(3010, 3010, "http");
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engine.resources([
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containerSet,
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service,
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])
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},
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}
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