I have the following code and I am trying to get all the hits from the elasticsearch. If I try to write without the query part it only gives me 10 results when I call .getHits.
val resultFuture = client.execute {
search in "reports/reportOutput" query{ termQuery("mainReportID", reportId.toString)}
}.await
Another issue is that the query part does not actually work and I get nothing. Here is a structure from my elasticsearch.
"hits": {
"total": 266,
"max_score": 1,
"hits":[
{
"_index": "reports",
"_type": "reportOutput",
"_id": "AUwjbAuKTetnUx12_a97",
"_score": 1,
"_source":
{
"displayName": "Classic BMW / MINI",
"model": "Cooper Clubman",
"dayInStock": "10",
"stockNumber": "Q323A",
"miles": "81093",
"interiorColorGeneric": "Black",
"year": "2009",
"trimLevel": "",
"mainReportID": "4d9e4fd3-7fdf-41c8-8c29-45c5acaf78b1",
"modelNumber": "",
"exteriorColorGeneric": "White",
"exteriorColor": "Pepper White",
"vin": "WMWML33509TX35944",
"make": "MINI",
"transmission": "A",
"exteriorColorCode": "850",
"interiorColor": "Gray/Carbon Black",
"interiorColorCode": "K8E1"
}
},
You can increase how many results are returned by setting a limit on the request, for example:
search in "index" limit 100
But the default limit of 10 is not set by elastic4s but by elasticsearch itself and you cannot change it to return all results by default.
Related
I got a collection of 10000 ca. docs, where each doc has the following format:
{
"_id": {
"$oid": "631edc6e207c89b932a70a26"
},
"name": "Ethereum",
"auditInfoList": [
{
"coinId": "1027",
"auditor": "Fairyproof",
"auditStatus": 2,
"reportUrl": "https://www.fairyproof.com/report/Covalent"
}
],
"circulatingSupply": 122335921.0615,
"cmcRank": 2,
"dateAdded": "2015-08-07T00:00:00.000Z",
"id": 1027,
"isActive": 1,
"isAudited": true,
"lastUpdated": 1662969360,
"marketPairCount": 6085,
"quotes": [
{
"name": "USD",
"price": 1737.1982544180462,
"volume24h": 14326453277.535921,
"marketCap": 212521748520.66168,
"percentChange1h": 0.62330307,
"percentChange24h": -1.08847937,
"percentChange7d": 10.96517745,
"lastUpdated": 1662966780,
"percentChange30d": -13.49374496,
"percentChange60d": 58.25153862,
"percentChange90d": 42.27475921,
"fullyDilluttedMarketCap": 212521748520.66,
"marketCapByTotalSupply": 212521748520.66168,
"dominance": 20.0725,
"turnover": 0.0674117,
"ytdPriceChangePercentage": -53.9168
}
],
"selfReportedCirculatingSupply": 0,
"slug": "ethereum",
"symbol": "ETH",
"tags": [
"mineable",
"pow",
"smart-contracts",
"ethereum-ecosystem",
"coinbase-ventures-portfolio",
"three-arrows-capital-portfolio",
"polychain-capital-portfolio",
"binance-labs-portfolio",
"blockchain-capital-portfolio",
"boostvc-portfolio",
"cms-holdings-portfolio",
"dcg-portfolio",
"dragonfly-capital-portfolio",
"electric-capital-portfolio",
"fabric-ventures-portfolio",
"framework-ventures-portfolio",
"hashkey-capital-portfolio",
"kenetic-capital-portfolio",
"huobi-capital-portfolio",
"alameda-research-portfolio",
"a16z-portfolio",
"1confirmation-portfolio",
"winklevoss-capital-portfolio",
"usv-portfolio",
"placeholder-ventures-portfolio",
"pantera-capital-portfolio",
"multicoin-capital-portfolio",
"paradigm-portfolio",
"injective-ecosystem"
],
"totalSupply": 122335921.0615
}
Im pulling updated version of it and, to aviod duplicates, im doing the following by using 'update_one'
for doc in new_doc_list:
CRYPTO_TEMPORARY_LIST.update_one(
{ "name" : doc['name']},
{ "$set": {
"lastUpdated": doc['lastUpdated']
}
},
upsert=True)
The problem is it's too slow.
I'm trying to figure out how to improve speed by using update_many but can't figure out how to set it up.
I Basically want to update every document x name. Completely change the doc and not the "lastUpdated" field would b even better.
Thanks guys <3
i'm new to the topic MongoDB and have 4 different problems importing a big (16GB) file (jsonl) into my MongoDB (simple PSA-Cluster).
Below attached you will find a sample entry from the mentiond JSON-Dump.
With this file which i get from an external provider I actually have 4 problems.
"hotel_id" is the key and should normally be (re-)named as "_id"
"hotel_id" should not be treated as string rather than as Number
"location" is not properly formatted (if i understood correctly the MongoDB Manual) as GeoJSON as it should be like
"location": {
"type": "Point",
"coordinates": [-93.26838,37.15845]
}
instead of
"location": {
"coordinates": {
"latitude": 37.15845,
"longitude": -93.26838
}
}
"dates" can this be used to efficiently update just the records which needs to be updated?
So my challenge is now to transform the data according to my needs before importing the data or at time of import, but in both cases of course as quickly as possible.
Therefore i searched a lot for hints and best practices, but i was not able to find a solution yet, maybe due to the fact that i'm a beginner with MongoDB.
I played around with "jq" to adjust the data and for example add the type which seems to be necessary for the location (point 3), but wasn't really successful.
cat dump.jsonl | ./bin/jq --arg typeOfField Point '.location + {type: $typeOfField}'
Beside that i was injecting a sample dump of round-about 500MB which took 1,5 mins when importing it the first time (empty database). If i run it in "upsert" mode it will take round-about 12 hours. So i was also wondering what is the best practice to import such a big JSON-dump?
Any help is appreciated!! :-)
Kind regards,
Lumpy
{
"hotel_id": "12345",
"name": "Test Hotel",
"address": {
"line_1": "123 Test St",
"line_2": "Apt A",
"city": "Test City",
},
"ratings": {
"property": {
"rating": "3.5",
"type": "Star"
},
"guest": {
"count": 48382,
"average": "3.1"
}
},
"location": {
"coordinates": {
"latitude": 22.54845,
"longitude": -90.11838
}
},
"phone": "555-0153",
"fax": "555-7249",
"category": {
"id": 1,
"name": "Hotel"
},
"rank": 42,
"dates": {
"added": "1998-07-19T05:00:00.000Z",
"updated": "2018-03-22T07:23:14.000Z"
},
"statistics": {
"11": {
"id": 11,
"name": "Total number of rooms - 220",
"value": "220"
},
"12": {
"id": 12,
"name": "Number of floors - 7",
"value": "7"
}
},
"chain": {
"id": -2,
"name": "Test Hotels"
},
"brand": {
"id": 2,
"name": "Test Brand"
}
}
I have a big issue, i don't know what to do...
What I wanna is to find all objects with Object2 name. I have Object 2 with name element.
What I wanna is to find all objects with the value X in the element name inside Object2. in the example is the value name is ="IWANTALLOBJECTSWITHTHISNAME"
the Json structure.
"objects": [
{
"_id": "5c69a62cf9acf00d00dbc02d",
"date": "2222-02-24T00:00:00.000Z",
"description": "22",
"Object1": {
"_id": "5c69a62cf9acf00d00dbc02b",
"date": "2222-02-24T00:00:00.000Z",
"user": "5c30fd5890bbd24a1c46c7ee",
"positionsObject1": [
{
"id": 1,
"Object2": {
"_id":"5c69a62cf9acf00d00dbc02c",
"name": "IWANTALLOBJECTSWITHTHISNAME"
},
"description": "22",
"value": 22
}
],
"id": 13,
"__v": 0
},
"user": "5c30fd5890bbd24a1c46c7ee",
"id": 7,
"__v": 0
}
]
I'm new in mongoDB and this query is really really hard. I tried everything. Thank very much for the help.
You can specify the path using dot notation:
db.col.find({ "objects.Object1.positionsObject1.Object2.name": "IWANTALLOBJECTSWITHTHISNAME" })
I got a big array with data in the following format:
{
"application": "myapp",
"buildSystem": {
"counter": 2361.1,
"hostname": "host.com",
"jobName": "job_name",
"label": "2361",
"systemType": "sys"
},
"creationTime": 1517420374748,
"id": "123",
"stack": "OTHER",
"testStatus": "PASSED",
"testSuites": [
{
"errors": 0,
"failures": 0,
"hostname": "some_host",
"properties": [
{
"name": "some_name",
"value": "UnicodeLittle"
},
<MANY MORE PROPERTIES>,
{
"name": "sun",
"value": ""
}
],
"skipped": 0,
"systemError": "",
"systemOut": "",
"testCases": [
{
"classname": "IdTest",
"name": "has correct representation",
"status": "PASSED",
"time": "0.001"
},
<MANY MORE TEST CASES>,
{
"classname": "IdTest",
"name": "normalized values",
"status": "PASSED",
"time": "0.001"
}
],
"tests": 8,
"time": 0.005,
"timestamp": "2018-01-31T17:35:15",
"title": "IdTest"
}
<MANY MORE TEST SUITES >,
]}
Where I can distinct three main structures with big data: TestSuites, Properties, and TestCases. My task is to sum all times from each TestSuite so that I can get the total duration of the test. Since the properties and TestCases are huge, the query cannot complete. I would like to select only the "time" value from TestSuites, but it kind of conflicts with the "time" of TestCases in my query:
db.my_tests.find(
{
application: application,
creationTime:{
$gte: start_date.valueOf(),
$lte: end_date.valueOf()
}
},
{
application: 1,
creationTime: 1,
buildSystem: 1,
"testSuites.time": 1,
_id:1
}
)
Is it possible to project only the "time" properties from TestSuites without loading the whole schema? I already tried testSuites: 1, testSuites.$.time: 1 without success. Please notice that TestSuites is an array of one element with a dictionary.
I already checked this similar post without success:
Mongodb update the specific element from subarray
Following code prints duration of each TestSuite:
query = db.my_collection.aggregate(
[
{$match: {
application: application,
creationTime:{
$gte: start_date.valueOf(),
$lte: end_date.valueOf()
}
}
},
{ $project :
{ duration: { $sum: "$testSuites.time"}}
}
]
).forEach(function(doc)
{
print(doc._id)
print(doc.duration)
}
)
Is it possible to project only the "time" properties from TestSuites
without loading the whole schema? I already tried testSuites: 1,
testSuites.$.time
Answering to your problem of prejecting only the time property of the testSuites document you can simply try projecting it with "testSuites.time" : 1 (you need to add the quotes for the dot notation property references).
My task is to sum all times from each TestSuite so that I can get the
total duration of the test. Since the properties and TestCases are
huge, the query cannot complete
As for your task, i suggest you try out the mongodb's aggregation framework for your calculations documents tranformations. The aggregations framework option {allowDiskUse : true} will also help you if you are proccessing "large" documents.
I'm in the process of developing Route Tracking/Optimization software for my refuse collection company and would like some feedback on my current data structure/situation.
Here is a simplified version of my MongoDB structure:
Database: data
Collections:
“customers” - data collection containing all customer data.
[
{
"cust_id": "1001",
"name": "Customer 1",
"address": "123 Fake St",
"city": "Boston"
},
{
"cust_id": "1002",
"name": "Customer 2",
"address": "123 Real St",
"city": "Boston"
},
{
"cust_id": "1003",
"name": "Customer 3",
"address": "12 Elm St",
"city": "Boston"
},
{
"cust_id": "1004",
"name": "Customer 4",
"address": "16 Union St",
"city": "Boston"
},
{
"cust_id": "1005",
"name": "Customer 5",
"address": "13 Massachusetts Ave",
"city": "Boston"
}, { ... }, { ... }, ...
]
“trucks” - data collection containing all truck data.
[
{
"truckid": "21",
"type": "Refuse",
"year": "2011",
"make": "Mack",
"model": "TerraPro Cabover",
"body": "Mcneilus Rear Loader XC",
"capacity": "25 cubic yards"
},
{
"truckid": "22",
"type": "Refuse",
"year": "2009",
"make": "Mack",
"model": "TerraPro Cabover",
"body": "Mcneilus Rear Loader XC",
"capacity": "25 cubic yards"
},
{
"truckid": "12",
"type": "Dump",
"year": "2006",
"make": "Chevrolet",
"model": "C3500 HD",
"body": "Rugby Hydraulic Dump",
"capacity": "15 cubic yards"
}
]
“drivers” - data collection containing all driver data.
[
{
"driverid": "1234",
"name": "John Doe"
},
{
"driverid": "4321",
"name": "Jack Smith"
},
{
"driverid": "3421",
"name": "Don Johnson"
}
]
“route-lists” - data collection containing all predetermined route lists.
[
{
"route_name": "monday_1",
"day": "monday",
"truck": "21",
"stops": [
{
"cust_id": "1001"
},
{
"cust_id": "1010"
},
{
"cust_id": "1002"
}
]
},
{
"route_name": "friday_1",
"day": "friday",
"truck": "12",
"stops": [
{
"cust_id": "1003"
},
{
"cust_id": "1004"
},
{
"cust_id": "1012"
}
]
}
]
"routes" - data collections containing data for all active and completed routes.
[
{
"routeid": "1",
"route_name": "monday1",
"start_time": "04:31 AM",
"status": "active",
"stops": [
{
"customerid": "1001",
"status": "complete",
"start_time": "04:45 AM",
"finish_time": "04:48 AM",
"elapsed_time": "3"
},
{
"customerid": "1010",
"status": "complete",
"start_time": "04:50 AM",
"finish_time": "04:52 AM",
"elapsed_time": "2"
},
{
"customerid": "1002",
"status": "incomplete",
"start_time": "",
"finish_time": "",
"elapsed_time": ""
},
{
"customerid": "1005",
"status": "incomplete",
"start_time": "",
"finish_time": "",
"elapsed_time": ""
}
]
}
]
Here is the process thus far:
Each day drivers begin by Starting a New Route. Before starting a new route drivers must first input data:
driverid
date
truck
Once all data is entered correctly the Start a New Route will begin:
Create new object in collection “routes”
Query collection “route-lists” for “day” + “truck” match and return "stops"
Insert “route-lists” data into “routes” collection
As driver proceeds with his daily stops/tasks the “routes” collection will update accordingly.
On completion of all tasks the driver will then have the ability to Complete the Route Process by simply changing “status” field to “active” from “complete” in the "routes" collection.
That about sums it up. Any feedback, opinions, comments, links, optimization tactics are greatly appreciated.
Thanks in advance for your time.
You database schema looks like for me as 'classic' relational database schema. Mongodb good fit for data denormaliztion. I guess when you display routes you loading all related customers, driver, truck.
If you want make your system really fast you may embedd everything in route collection.
So i suggest following modifications of your schema:
customers - as-is
trucks - as-is
drivers - as-is
route-list:
Embedd data about customers inside stops instead of reference. Also embedd truck. In this case schema will be:
{
"route_name": "monday_1",
"day": "monday",
"truck": {
_id = 1,
// here will be all truck data
},
"stops": [{
"customer": {
_id = 1,
//here will be all customer data
}
}, {
"customer": {
_id = 2,
//here will be all customer data
}
}]
}
routes:
When driver starting new route copy route from route-list and in addition embedd driver information:
{
//copy all route-list data (just make new id for the current route and leave reference to routes-list. In this case you will able to sync route with route-list.)
"_id": "1",
route_list_id: 1,
"start_time": "04:31 AM",
"status": "active",
driver: {
//embedd all driver data here
},
"stops": [{
"customer": {
//all customer data
},
"status": "complete",
"start_time": "04:45 AM",
"finish_time": "04:48 AM",
"elapsed_time": "3"
}]
}
I guess you asking yourself what do if driver, customer or other denormalized data changed in main collection. Yeah, you need update all denormalized data within other collections. You will probably need update billions of documents (depends on your system size) and it's okay. You can do it async if it will take much time.
What benfits in above data structure?
Each document contains all data that you may need to display in your application. So, for instance, you no need load related customers, driver, truck when you need display routes.
You can make any difficult queries to your database. For example in your schema you can build query that will return all routes thats contains stops in stop of customer with name = "Bill" (you need load customer by name first, get id, and look by customer id in your current schema).
Probably you asking yourself that your data can be unsynchronized in some cases, but to solve this you just need build a few unit test to ensure that you update your denormolized data correctly.
Hope above will help you to see the world from not relational side, from document database point of view.