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Description | Mean (ms) | p95 (ms) | Transactions/s | |
---|---|---|---|---|
Writing to 3 collections with transactions | 8 | 9 | 3625 | |
Writing to 3 collections without transactions | 17 | 17 | 1905 |
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Schema | Mean (ms) | p95(ms) | Transactions/s | |
---|---|---|---|---|
Reading from Transaction (including BalanceHistory) | 27 | 50 | 4582 | |
Reading from Balance with 10 cached Balance Histories | 6 | 7 | 20631 |
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Schema | Mean(ms) | p95(ms) | Transactions/s | |
---|---|---|---|---|
Original - 3 writes | 11 | 12 | 4161 | |
Two write version | 10 | 12 | 5822 |
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Server | vCPU | RAM | IOPS | Transactions/s | Mean (ms) | p95(ms) | Notes | |
---|---|---|---|---|---|---|---|---|
M30 | 2 | 8 | 3000 | 1265 | 25 | 28 | Higher latency got a little more throughput, more client threads | |
M50 | 8 | 32 | 3000 | 3794 | 12 | 14 | 60% CPU, notable checkpointing, notable read cache, disk latency >10ms | |
M50+4TB Disk | 8 | 32 | 8000 | 4161 | 11 | 12 | CPU 83%, no checkpoints | |
M60 +4TB Disk | 16 | 64 | 8000 | 6721 | 9 | 10 | Not quite linear performance improvement | |
M80+4TB Disk | 32 | 128 | 8000 | 6995 | 9 | 10 | Unsure what limit was at this point, probably disk; shows M60 + sharding better option |
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public String getMOTResultInJSON(String identifier) { | |
long identifierLong; | |
try { | |
identifierLong = Long.valueOf(identifier); | |
Bson byIdQuery = Filters.eq("vehicleid", identifierLong); | |
testObj = testresults.find(byIdQuery).limit(1).first(); | |
if (testObj != null) { | |
return testObj.toJson(); | |
} | |
} catch (Exception e) { |
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//Query From https://data.dft.gov.uk/anonymised-mot-test/MOT_user_guide_v4.docx | |
private final String getlatestByVehicleSQL = "select " + | |
"tr.*, " + | |
"ft.FUEL_TYPE, " + | |
"tt.TESTTYPE AS TYPENAME, " + | |
"to2.RESULT, " + | |
"ti.*, " + | |
"fl.*, " + "tid.MINORITEM,tid.RFRDESC,tid.RFRLOCMARKER,tid.RFRINSPMANDESC,tid.RFRADVISORYTEXT,tid.TSTITMSETSECID, " + | |
"b.ITEMNAME AS LEVEL1, " + | |
"c.ITEMNAME AS LEVEL2, " + |
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{ | |
CustomerId: 123, | |
Name : { First: "John", Last: "Page"}, | |
TotalSpend: 261, | |
Purchases: [ | |
{ PurchaseID: 519, Item: 25, Quantity: 2, Price: 50 }, | |
{ PurchaseID: 530, Item: 25, Quantity: 1, Price: 180 }, | |
{ PurchaseId: 640, Item: 9, Quantity: 1, Price: 31 } | |
] | |
} |
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exports = async function() { | |
let APIKEY = context.values.get('ifftkey_value') | |
const collection = context.services.get("mongodb-atlas").db("energy").collection("meter"); | |
const docs = await collection.find({ type: "electric" }).sort({_id:-1}).limit(1).toArray(); | |
if(docs.length == 1) | |
{ | |
if(docs[0].solarwatts > 800) { | |
console.log("turning off lights") | |
const response = context.http.get({ url: `https://maker.ifttt.com/trigger/AllLightsOff/with/key/${APIKEY}` }) | |
} |
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[ | |
{ $match: { type: "electric" } }, | |
{ $unwind: "$devices" }, | |
{ $match: { "devices.watts": { $gt: 0 } } }, | |
{ $setWindowFields: { sortBy: { date: 1 }, partitionBy: "$devices.name", | |
output: { totalUsage: { $integral: { input: "$devices.watts", unit: "hour" } } } }}, | |
] |
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[ | |
{ $match: { type: "electric" } }, | |
{ | |
$setWindowFields: | |
{ | |
sortBy: { date: 1 }, | |
output: { | |
smoothsumusage: { | |
$expMovingAvg: { input: { $sum: "$devices.watts"} , alpha: 0.9} | |
} |
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