I'm using MiniMongo through Meteor, and I'm trying to create a frequency table based off of a dynamic set of queries.
I have two main fields, localHour and localDay. I expect many overlaps, and I'd like to determine where the most overlaps occur. My current method of doing this is so.
if(TempStats.findOne({
localHour: hours,
localDay: day
})){//checks if there is already some entry on the same day/hour
TempStats.update({//if so, we just increment frequency
localHour: hours,
localDay: day
},{
$inc: {freq: 1}
})
} else {//if nothing exists yet, we put in a new entry
TempStats.insert({
localHour: hours,
localDay: day,
freq: 1
});
}
Essentially, this code runs every time I have new data I want to insert. It works fine at the moment, in that, after all data is inserted, I can sort by frequency to find what set of hours & days occurs the most often (TempStats.find({}, {sort: {freq: -1}}).fetch()).
However, I'm looking more for a way to search by frequency for any key. For instance, searching for the day which everything occurs on the most often as opposed to both the date and hour. With my current way of doing this, I would need to have multiple databases and different methods of inserting for each, which is a bit ridiculous. Is there a Mongo (specifically MiniMongo) solution to do frequency maps based on keys?
Thanks!
It looks like miniMongo does not in fact support aggregation, which makes this kind of operation difficult. One way to go about it would be aggregating yourself at the end of each day and inserting that aggregate record into your db (without the hour field or with it set to something like -1). Conversely as wastefully you could also update that record at the time of each insert. This would allow you to use the same collection for both and is fairly common in other dbs.
Also you should consider #nickmilon's first suggestion since the use of an upsert statement with the $inc operator would reduce your example to a single operation per data point.
a small note on your code: the part that comes as an else statement is not really required your update will do the complete job if you combine it with the option upsert=true it will insert a new document and $inc will set the freq field to 1 as desired see: here and here
for alternative ways to count your frequencies: assuming you store the date as a datetime object I would suggest to use an aggregation (I am not sure if they added support for aggregation yet in minimongo) but there are solutions then with aggregation you can use datetime operators as
$hour, $week, etc for filtering and $count to count the frequencies without you having to keep counts in the database.
This is basically a simple map-reduce problem.
First, don't separate the derived data into 2 fields. This violates DB best practices. If the data comes to you this way, use it to create a Date object. I assume you have a bunch of collections that are being subscribed to and then you aggregate all those into this temporary local collection. This is the mapping of the map-reduce pattern. At this point, since your query in unknown, it's a waste of CPU (even though it's your client) to aggregate. Map first, reduce second. What you should have is a collection full of datetimes. call it TempMapCollection if you wish. Now, use a forEach() and pass in your reduce function (by day, by hour, etc).
You can reduce into another local collection, or into a javascript object. I like using collections, but if the objects are complex, you'll get EJSON errors all up in there. Since your objects are nothing more than a datetime, let's use collections.
so you've got something like:
TempMapCollection.find().forEach(function(doc) {
var date = doc.dateTime.getDate();
TempReduceCollection.upsert({timequery: hours}, {$inc: {freq: 1}});
})
Now query your reduce collection. This has the added benefit that you won't have to re-map if you want to do 2 unique queries.
Related
I have a query:
const q = query(
collection(db, '/listings'),
where('price', '>=', 4000),
orderBy('price', 'desc'),
orderBy('geoHash'),
startAt(b[0]),
endAt(b[1]),
limit(DEFAULT_LIMIT_OF_LISTINGS),
) as Query<IListing>;
If I remove
where('price', '>=', 4000),"
it works fine with the geoHash condition.
if I remove geoHash condition it works fine as well with the price condition.
Why they are not working together?
I expect to get all documents with a price greater than 4000 in the given area.
Firestore queries can only contain one relational condition (>=, >, etc) because such conditions can only be evaluated on the first field in an index. Since you need a relational/range condition for the geohash already, you can't also have a >= condition on price.
The common options to work around this are:
Perform the filter on the second condition in your application code, so that you first get all documents that are in range, and then in your application remove the ones whose price is out of range.
Add a field to your database that allows the use-case you want. For example, if you add a field isPriceOver4000: true you can use an equality condition .where('isPriceOver4000', '==', true).
That last option may feel wrong, but is actually quite common when using NoSQL to modify and augment your data model to fit with your use-case. Of course you'll want to find the best model for your needs, for example you might want an array (or map subfield) of price tags that users can filter on.
Alternatively, you can create similar buckets of regions, and query the location on that instead of geohash, and then use the >= on price.
Few restrictions are applied to .orderBy() parameter, have a look at the official documentation.
Here in this case, the you can only order by price and not geohash, if I understood the concept correctly, please go through official docs.
I have a collection whose documents look something like this:
count: number
first: timestamp
last: timestamp
The first value should (almost) never change after the document's creation.
In a batch write operation, I am trying to update documents in this collection, or create those documents that do not yet exist. Something like
batch.setData([
"count": FieldValue.increment(someInteger),
"first": someTimestamp,
"last": someTimestamp
], forDocument: someDocumentRef, mergeFields: ["count","last"])
My hope was that by excluding first from the mergeFields array, Firestore would set count and last by merging it into an existing document or making a new one, and set first only if it had no previous value (i.e., the document didn't exist before this operation). It is clear to me now that this is not the case, and instead first is completely ignored. Now I'm left wondering what the Firestore team intended for this situation.
I know that I could achieve this with a Transaction, but that doesn't tie in very well with my batch write. Are Transactions my only option, or is there a better way to achieve this?
I have created timestamps and other data in my documents and I handle this using separate create and update functions rather than trying to do it all at once.
The initial creation function includes the created date etc and then subsequent updates use the non-destructive update, so just omit any fields in the update payload you do not want to overwrite.
eg. to create:
batch.set(docRef, {created: someTimestamp, lastUpdate: someTimestamp})
then to update:
batch.update(docRef, {lastUpdate: someTimestamp, someOtherField: someData})
This will not overwrite the creationDate field or any other fields, but will create the someOtherField if it does not exist.
If you have a need to do a "only update existing fields" update after the document is created for the first time then currently you have to read the document first to find out if the fields exist and then create an update payload which will patch the only the desired fields. This can be done in a transaction or you can write this logic yourself, depending on your needs.
I need to do a query where I can show only specific data using an 'AND' statement or equivalent to it. I have taken the example which is displayed in the Firebase Documentation.
// Find all dinosaurs whose height is exactly 25 meters.
var ref = firebase.database().ref("dinosaurs");
ref.orderByChild("height").equalTo(25).on("child_added", function(snapshot) {
console.log(snapshot.key);
});
I understand this line is going to retrieve all the dinosaurs whose height is exactly 25, BUT, I need to show all dinosaurs whose height is '25' AND name is 'Dino'. Is there any way to retrieve this information?
Thanks in advance.
Actually firebase only supports filtering/ordering with one propery, but if you want to filter with more than one property like you said I want to filter with age and name, you have to use composite keys.
There is a third party library called querybase which gives you some capabilities of multy property filtering. See https://github.com/davideast/Querybase
You cannot query by multiple keys.
If you need to sort by two properties your options are:
Create a hybrid key. In reference to your example, if you wanted to get all 'Dino' and height '25' then you would create a hybrid name_age key which could look something like Dino_25. This will allow you to query and search for items with exactly the same value but you lose the ability for ordering (i.e. age less than x).
Perform one query on Firebase and the other client side. You can query by name on Firebase and then iterate through the results and keep the results that match age 25.
Without knowing much about your schema I would advise you to make sure you're flattening your data sufficiently. Often I have found that many multi-level queries can be solved by looking at how I'm storing the data. This is not always the case and sometimes you may just have to take one of the routes I have mentioned above.
I want to implement pagination on top of a MongoDB. For my range query, I thought about using ObjectIDs:
db.tweets.find({ _id: { $lt: maxID } }, { limit: 50 })
However, according to the docs, the structure of the ObjectID means that "ObjectId values do not represent a strict insertion order":
The relationship between the order of ObjectId values and generation time is not strict within a single second. If multiple systems, or multiple processes or threads on a single system generate values, within a single second; ObjectId values do not represent a strict insertion order. Clock skew between clients can also result in non-strict ordering even for values, because client drivers generate ObjectId values, not the mongod process.
I then thought about querying with a timestamp:
db.tweets.find({ created: { $lt: maxDate } }, { limit: 50 })
However, there is no guarantee the date will be unique — it's quite likely that two documents could be created within the same second. This means documents could be missed when paging.
Is there any sort of ranged query that would provide me with more stability?
It is perfectly fine to use ObjectId() though your syntax for pagination is wrong. You want:
db.tweets.find().limit(50).sort({"_id":-1});
This says you want tweets sorted by _id value in descending order and you want the most recent 50. Your problem is the fact that pagination is tricky when the current result set is changing - so rather than using skip for the next page, you want to make note of the smallest _id in the result set (the 50th most recent _id value and then get the next page with:
db.tweets.find( {_id : { "$lt" : <50th _id> } } ).limit(50).sort({"_id":-1});
This will give you the next "most recent" tweets, without new incoming tweets messing up your pagination back through time.
There is absolutely no need to worry about whether _id value is strictly corresponding to insertion order - it will be 99.999% close enough, and no one actually cares on the sub-second level which tweet came first - you might even notice Twitter frequently displays tweets out of order, it's just not that critical.
If it is critical, then you would have to use the same technique but with "tweet date" where that date would have to be a timestamp, rather than just a date.
Wouldn't a tweet "actual" timestamp (i.e. time tweeted and the criteria you want it sorted by) be different from a tweet "insertion" timestamp (i.e. time added to local collection). This depends on your application, of course, but it's a likely scenario that tweet inserts could be batched or otherwise end up being inserted in the "wrong" order. So, unless you work at Twitter (and have access to collections inserted in correct order), you wouldn't be able to rely just on $natural or ObjectID for sorting logic.
Mongo docs suggest skip and limit for paging:
db.tweets.find({created: {$lt: maxID}).
sort({created: -1, username: 1}).
skip(50).limit(50); //second page
There is, however, a performance concern when using skip:
The cursor.skip() method is often expensive because it requires the server to walk from the beginning of the collection or index to get the offset or skip position before beginning to return result. As offset increases, cursor.skip() will become slower and more CPU intensive.
This happens because skip does not fit into the MapReduce model and is not an operation that would scale well, you have to wait for a sorted collection to become available before it can be "sliced". Now limit(n) sounds like an equally poor method as it applies a similar constraint "from the other end"; however with sorting applied, the engine is able to somewhat optimize the process by only keeping in memory n elements per shard as it traverses the collection.
An alternative is to use range based paging. After retrieving the first page of tweets, you know what the created value is for the last tweet, so all you have to do is substitute the original maxID with this new value:
db.tweets.find({created: {$lt: lastTweetOnCurrentPageCreated}).
sort({created: -1, username: 1}).
limit(50); //next page
Performing a find condition like this can be easily parallellized. But how to deal with pages other than the next one? You don't know the begin date for pages number 5, 10, 20, or even the previous page! #SergioTulentsev suggests creative chaining of methods but I would advocate pre-calculating first-last ranges of the aggregate field in a separate pages collection; these could be re-calculated on update. Furthermore, if you're not happy with DateTime (note the performance remarks) or are concerned about duplicate values, you should consider compound indexes on timestamp + account tie (since a user can't tweet twice at the same time), or even an artificial aggregate of the two:
db.pages.
find({pagenum: 3})
> {pagenum:3; begin:"01-01-2014#BillGates"; end:"03-01-2014#big_ben_clock"}
db.tweets.
find({_sortdate: {$lt: "03-01-2014#big_ben_clock", $gt: "01-01-2014#BillGates"}).
sort({_sortdate: -1}).
limit(50) //third page
Using an aggregate field for sorting will work "on the fold" (although perhaps there are more kosher ways to deal with the condition). This could be set up as a unique index with values corrected at insert time, with a single tweet document looking like
{
_id: ...,
created: ..., //to be used in markup
user: ..., //also to be used in markup
_sortdate: "01-01-2014#BillGates" //sorting only, use date AND time
}
The following approach wil work even if there are multiple documents inserted/updated at same millisecond even if from multiple clients (which generates ObjectId). For simiplicity, In following queries I am projecting _id, lastModifiedDate.
First page, fetch the result Sorted by modifiedTime (Descending), ObjectId (Ascending) for fist page.
db.product.find({},{"_id":1,"lastModifiedDate":1}).sort({"lastModifiedDate":-1, "_id":1}).limit(2)
Note down the ObjectId and lastModifiedDate of the last record fetched in this page. (loid, lmd)
For sencod page, include query condition to search if (lastModifiedDate = lmd AND oid > loid ) OR (lastModifiedDate < loid)
db.productfind({$or:[{"lastModifiedDate":{$lt:lmd}},{"_id":1,"lastModifiedDate":1},{$and:[{"lastModifiedDate":lmd},{"_id":{$gt:loid}}]}]},{"_id":1,"lastModifiedDate":1}).sort({"lastModifiedDate":-1, "_id":1}).limit(2)
repeat same for subsequent pages.
ObjectIds should be good enough for pagination if you limit your queries to the previous second (or don't care about the subsecond possibility of weirdness). If that is not good enough for your needs then you will need to implement an ID generation system that works like an auto-increment.
Update:
To query the previous second of ObjectIds you will need to construct an ObjectID manually.
See the specification of ObjectId http://docs.mongodb.org/manual/reference/object-id/
Try using this expression to do it from a mongos.
{ _id :
{
$lt : ObjectId(Math.floor((new Date).getTime()/1000 - 1).toString(16)+"ffffffffffffffff")
}
}
The 'f''s at the end are to max out the possible random bits that are not associated with a timestamp since you are doing a less than query.
I recommend during the actual ObjectId creation on your application server rather than on the mongos since this type of calculation can slow you down if you have many users.
I have build a pagination using mongodb _id this way.
// import ObjectId from mongodb
let sortOrder = -1;
let query = []
if (prev) {
sortOrder = 1
query.push({title: 'findTitle', _id:{$gt: ObjectId('_idValue')}})
}
if (next) {
sortOrder = -1
query.push({title: 'findTitle', _id:{$lt: ObjectId('_idValue')}})
}
db.collection.find(query).limit(10).sort({_id: sortOrder})
I'm using what seems to be a common trick for creating a join view:
// a Customer has many Orders; show them together in one view:
function(doc) {
if (doc.Type == "customer") {
emit([doc._id, 0], doc);
} else if (doc.Type == "order") {
emit([doc.customer_id, 1], doc);
}
}
I know I can use the following query to get a single customer and all related Orders:
?startkey=["some_customer_id"]&endkey=["some_customer_id", 2]
But now I've tied my query very closely to my view code. Is there a value I can put where I put my "2" to more clearly say, "I want everything tied to this Customer"? I think I've seen
?startkey=["some_customer_id"]&endkey=["some_customer_id", {}]
But I'm not sure that {} is certain to sort after everything else.
Credit to cmlenz for the join method.
Further clarification from the CouchDB wiki page on collation:
The query startkey=["foo"]&endkey=["foo",{}] will match most array keys with "foo" in the first element, such as ["foo","bar"] and ["foo",["bar","baz"]]. However it will not match ["foo",{"an":"object"}]
So {} is late in the sort order, but definitely not last.
I have two thoughts.
Use timestamps
Instead of using simple 0 and 1 for their collation behavior, use a timestamp that the record was created (assuming they are part of the records) a la [doc._id, doc.created_at]. Then you could query your view with a startkey of some sufficiently early date (epoch would probably work), and an endkey of "now", eg date +%s. That key range should always include everything, and it has the added benefit of collating by date, which is probably what you want anyways.
or, just don't worry about it
You could just index by the customer_id and nothing more. This would have the nice advantage of being able to query using just key=<customer_id>. Sure, the records won't be collated when they come back, but is that an issue for your application? Unless you are expecting tons of records back, it would likely be trivial to simply pluck the customer record out of the list once you have the data retrieved by your application.
For example in ruby:
customer_records = records.delete_if { |record| record.type == "customer" }
Anyways, the timestamps is probably the more attractive answer for your case.
Rather than trying to find the greatest possible value for the second element in your array key, I would suggest instead trying to find the least possible value greater than the first: ?startkey=["some_customer_id"]&endkey=["some_customer_id\u0000"]&inclusive_end=false.
CouchDB is mostly written in Erlang. I don't think there would be an upper limit for a string compound/composite key tuple sizes other than system resources (e.g. a key so long it used all available memory). The limits of CouchDB scalability are unknown according to the CouchDB site. I would guess that you could keep adding fields into a huge composite primary key and the only thing that would stop you is system resources or hard limits such as maximum integer sizes on the target architecture.
Since CouchDB stores everything using JSON, it is probably limited to the largest number values by the ECMAScript standard.All numbers in JavaScript are stored as a floating-point IEEE 754 double. I believe the 64-bit double can represent values from - 5e-324 to +1.7976931348623157e+308.
It seems like it would be nice to have a feature where endKey could be inclusive instead of exclusive.
This should do the trick:
?startkey=["some_customer_id"]&endkey=["some_customer_id", "\uFFFF"]
This should include anything that starts with a character less than \uFFFF (all unicode characters)
http://wiki.apache.org/couchdb/View_collation