I have a headache for a idea how to properly sort data from a MongoDB. It is using 2dsphere index and has timestamp createdAt. The goal is to show latest pictures (that what this collection is about, just a field mediaUrl...) but it has to be close to the user. I'm not very familiar with complex MongoDB aggregation queries so I thought here's a good place to ask. Sorting with $near shows only items sorted by distance. But there's a upload time, e.g. if item is 5 min fresh but is like 500 meters far than older item it still should be sorted higher.
Ugly way would be to iterate every few hundreds meters and collect data but maybe there's a smarter way?
So if I am correct you want to be able to sort on 2 fields ?
distance
timestamp
You should check out this method:
https://docs.mongodb.com/manual/reference/operator/aggregation/sort/
It allows you to sort multiple columns.
Related
I am trying to sort the result of my mongoDB query and add a ranking based on that sorting. Currently I only call .find().sort({total: 1}) and this gives me the correct ordering of the documents. But is it possible to "add a field" based on that sorting (basically a ranking field, starting from 1 and counting up)? I tried googling but didnt found anything that suits for this purpose.
Thanks in advance.
Looking for a way to view the results of a query where the displayed fields in the doc are ordered (lexicographically in my case).
Example:
I'm getting back from a query one document, which is what I need. This document has 30 fields and I'm looking to see the value in one of them. My issue is that the order of the fields is, well, kinda random. Not sorted in any way I'm aware of.
I'm working on my app and I just ran into a dilemma regarding what's the best way to handle indexes for firestore.
I have a query that search for publication in a specify community that contains at least one of the tag and in a geohash range. The index for that query looks like this:
community Ascending tag Ascending location.geohash Ascending
Now if my user doesnt need to filter by tag, I run the query without the arrayContains(tag) which prompt me to create another index:
community Ascending location.geohash Ascending
My question is, is it better to create that second index or, to just use the first one and specifying all possible tags in arrayContains in the query if the user want no filters on tag ?
Neither is pertinently better, but it's a typical space vs time tradeoff.
Adding the extra tags in the query adds some overhead there, but it saves you the (storage) cost for the additional index. So you're trading some small amount of runtime performance for a small amount of space/cost savings.
One thing to check is whether the query with tags can actually run on just the second index, as Firestore may be able to do a zigzag merge join. In that case you could only keep the second, smaller index and save the runtime performance of adding additional clauses, but then get a (similarly small) performance difference on the query where you do specify one or more tags.
I'm currently trying to use MongoDB Compass to query my collection. However, I seem to be only able to filter the data.
Is there any way for me to sort the data as well? I would like to sort my data in ascending order using one of my data fields.
If MongoDB Compass isn't the best way to order a collection, what other GUI could I use?
Using MongoDB Compass 1.7 or newer, you can sort (and project, skip, or limit) results by choosing the Documents tab and expanding the Options.
To sort in ascending order by a field myField, use { myField:1 }. Any of the usual cursor sort() options can be provided, including ordering results by multiple fields.
Note: options like sort and skip are not available in the default Schema tab because this view uses sampling to find a random set of documents, as opposed to the Documents view which displays a specific query result.
sorry to have to ask this but I am new to MongoDB (only have experience with relational databases) and was just curious as to how you would structure your MongoDB.
The documents will be in the format of JSONs with some of the following fields:
{
"url": "http://....",
"text": "entire ad content including HTML (very long)",
"body": "text (50-200 characters)",
"date": "01/01/1990",
"phone": "8001112222",
"posting_title": "buy now"
}
Some of the values will be very long strings.
Each document is essentially an ad from a certain city. We are storing all ads for a lot of big cities in the US (about 422). We are storing more ads every day, and the amount of ads per city varies from as little as 0 to as big as 2000. The average is probably around 700-900.
We need to do the following types of queries, in almost instant time (if possible):
Get all ads for any specific city, for any specific date range.
Get all ads that were posted by a specific phone number, for any city, for any date range.
What would you recommend? I'm thinking I should have 422 collections - one for each city. I'm just worried about the query time when we query for phone numbers because it needs to go through each collection. I have an iterable list of all collection names.
Or would it be faster to just have one collection so that I don't have to switch through 422 collections?
Thank you so much, everyone. I'm here to answer any questions!
EDIT:
Here is my "iterating through all collections" snippet:
for name in glob.glob("Data\Nov. 12 - 5pm\*"):
val = name.split("5pm")[1].split(".json")[0][1:]
coll = db[val]
# Add into collection here...
MongoDB does not offer any operations which get results from more than one collection, so putting your data in multiple collections is not advisable in this case.
You can considerably speed up all the use-cases you mentioned by creating indexes for them. When you have a very large dataset and always query for exact equality, then hashed indexes are the fastest.
When you query a range of dates (between day x and day y), you should use the Date type and not strings, because this not just allows you to use lots of handy date operators in aggregation but also allows you to speed up ranged queries and sorts with ascending or descending indexes.
Maybe I'm missing something, but wouldn't making "city" a field in your JSON solve your problem? That way you only need to do something like this db.posts.find({ city: {$in: ['Boston', 'Michigan']}})