Microsoft's documentation of Managing indexing in Azure Cosmos DB's API for MongoDB states that:
Azure Cosmos DB's API for MongoDB server version 3.6 automatically
indexes the _id field, which can't be dropped. It automatically
enforces the uniqueness of the _id field per shard key.
I'm confused about the reasoning behind "per shard key" part. I see it as "you're unique field won't be globally unique at all" because if I understand it correctly, if I have the Guid field _id as unique and userId field as the partition key then I can have 2 elements with the same ID provided that they happen to belong to 2 different users.
Is it that I fail to pick the right partition key? Because in my understanding partition key should be the field that is the most frequently used for filtering the data. But what if I need to select the data from the database only by having the ID field value? Or query the data for all users?
Is it the inherent limits in distributed systems that I need to accept and therefore remodel my process of designing a database and programming the access to it? Which in this case would be: ALWAYS query your data from this collection not only by _id field but first by userId field? And not treat my _id field alone as an identifier but rather see an identifier as a compound of userId and _id?
TL;DR
Is it the inherent limits in distributed systems that I need to accept and therefore remodel my process of designing a database and programming the access to it? Which in this case would be: ALWAYS query your data from this collection not only by _id field but first by userId field? And not treat my _id field alone as an identifier but rather see an identifier as a compound of userId and _id?
Yes. Mostly.
Longer version
While this id not field not being unique is not intuitive at first sight, it actually makes sense, considering CosmosDB seeks unlimited scale for pinpoint GET/PUT operations. This requires the partitions to act independently and this is where a lot of the magic comes from. If id or other unique constraint uniqueness would have been enforced globally, then every document change would have to coordinate with all other partitions and that would no longer be optimal or predictable in endless scale.
I also think this design decision of separation of data is in alignment with the schemaless distributed mindset of CosmosDB. If you use CosmosDB then embrace this and avoid trying to force cross-document relation constraints to it. Manage them in data/api design and client logic layer instead. For example, by using a guid for id.
About partition key..
Is it that I fail to pick the right partition key? [...] partition key should be the field that is the most frequently used for filtering the data.
It depends;). You have to think also for worst query performance, not only the "most frequently" used ones. Make sure MOST queries can go directly to correct partition, meaning you MUST know the exact target partition key before making those queries, even for those "get by id"-queries. Measure the cost for left cross-partition queries on realistic data set.
It is difficult to say whether userId is a good key or not. It most likely is known in advance and could be included to get-by-id queries, so it's good in that sense. But you should also consider:
hot partition - all single user queries would go to single partition, no scale out there.
partition size - single user data most likely grows-and-grows-and-grows. Partitions have a max size limits and working within those target partitions will become costlier over time.
So, if possible, I would define smaller partitions to distribute the load further. Maybe consider using a composite partition key or similar tactics to split user partition to multiple smaller ones. Or to the very extreme of having id itself a partition key, which is good for writes and get-by-id but less optimal for everything else.
.. just always make sure to have the chosen partition key at hand.
Related
I have a collection of documents that looks like the following:
There is one document per VIN/SiteID and our access pattern is showing all documents
at a specific site. I see two potential partition keys we could choose from:
SiteID - We only have 75 sites so the cardinality is not very high. Also, the doucments are not very big so the 10GB limit is probably OK.
SiteID/VIN: The data is now more evenly distributed but now that means each logical partition will only store one item. is this an anti-pattern? also, so support our access pattern we will need to use a cross-partition query. again, the data set is small so is this a problem?
Based on what I am describing, which partition key makes more sense?
Any other suggestions would be greatly appreciated!
Your first option makes a lot of sense and could be a good partition key but the words "probably OK" don't really breed confidence. Remember, the only way to change the partition key is to migrate to a new collection. If you can take that risk then SiteId (which I'm guessing you will always have) is a good partition key.
If you have both VIN and SiteId when you are doing the reading or querying then this is the safer combination. There is no problem with having each logical partition to store one item per se. It's only a problem when you are doing cross partition queries. If you know both VIN and SiteId in your queries then it's a great plan.
You also have to remember that your RUs are evenly split between your partitions inside a collection.
Consider I have very huge table that needs to be sharded across the RDBMS cluster. I need to decide on the partitioning key on which to shard the table across. Obviously this partition key can’t be an artificial key (example: auto-generated primary key column), because the application needs to hold the logic of figuring out the shard depends on the natural key from request data. Consider the following situation
If the natural key is not evenly distributed in the system
a) Is it a good idea to even consider this table for sharding ?
Is there a way to generate a GUID based on the natural key and evenly distribute it across the cluster?
what can be an efficient algorithm to generate a GUID based on the natural key.
If the key is not evenly distributed it might not have any difference whether the table is partitioned or not. It will have to read almost same amount of rows in order to fulfil the query. Remember, partitioning will not always increase the performance. Reading across partitions will might be slower. So make sure you analyse all the query needs before selecting the partition key.
I can't recall any function which can generate partition key for this case. There are functions to generate GUIDs or MD5 for your data but the result will be worst than natural key that you have. The results will be more towards to unique values. Also it will drop the performance as each and every request it has to run additional logics.
Also please consider purging old or unused data. Once that is done you might not have partitioning need.
I'm working on a multi-tenant application running on mongodb. Each tenant can create multiple applications. The schema for most of the collections reference other collections via ObjectIDs. I'm thinking of manually creating a shard key with every record insertion in the following format:
(v3 murmurhash of the record's ObjectId) + (app_id.toHexString())
Is this good enough to ensure that records for any particular application will likely end up on the same shard?
Also, what happens if a particular application grows super large compared to all others on the shard?
If you use a hash based shard key with the input constantly changing (ObjectID can generally be considered to be unique for each record), then you will get no locality of data on shards at all (except by coincidence), though it will give you great write throughput by randomly distributing writes across all shards. That's basically the trade off with this kind of approach, the same is true of the built in hash based sharding, those trade offs don't change just because it is a manual hash constructed of two fields.
Basically because MongoDB uses range based chunks to split up the data for a given shard key you will have sequential ranges of hashes used as chunks in this case. Assuming your hash is not buggy in some way, then the data in a single sequential range will basically be random. Hence, even within a single chunk you will have no data locality, let alone on a shard, it will be completely random (by design).
If you wanted to be able to have applications grouped together in ranges, and hence more likely to be on a particular shard then you would be better off to pre-pend the app_id to make it the leftmost field in a compound shard key. Something like sharding on the following would (based on the limited description) be a good start:
{app_id : 1, _id : 1}
Though the ObjectID is monotonically increasing (more discussion on that here) over time, if there are a decent number of application IDs and you are going to be doing any range based or targeted queries on the ObjectID, then it might still work well though. You may also want to have other fields included based on your query pattern.
Remember that whatever your most common query pattern is, you want to have the shard key (ideally) satisfy it if at all possible. It has to be indexed, it has be used by the mongos to decide to route the query (if not, then it is scatter/gather), so if you are going to constantly query on app_id and _id then the above shard key makes a lot of sense.
If you go with the manual hashed key approach not only will you have a random distribution, but unless you are going to be querying on that hash it's not going to be very useful.
I've been thinking about selecting the best shard key (through a compound index) for my data and thought the combination of the document creation date combined with a customer no. (or invoice no.) would be a good combination. IF MongoDB would consider the customer no as a string backwards ie.:
90043 => 34009
90044 => 44009
90045 => 54009
etc.
Index on the The creation date would ensure that relatively new data are kept in memory and the backward customer no would help MongoDB to distribute the data/load across the cluster.
Is this a correct assumption? and if so... would I need to save my customer no reversed for it to be distributed the way I expect?
Regarding your specific question of "would I need to save my customer no reversed for it to be distributed the way I expect?", no - you would not.
Even with the relatively narrow spread of customer number values you listed, if you use customerNumber in your compound key, MongoDB will break apart the data into chunks and distribute these accordingly. As long as the data associated with customerNumber are relatively evenly distributed (e.g., one user doesn't dominate the system), you will get the shard balancing you desire.
I would consider either your original choice (minus the string reversal) or Dan's choice (using the built-in ObjectId instead of timestamp) as good candidates for your compound key.
from what I have read in the documentation the MongoId is already time based.
Therfore you can add the _id to your compound key like this: (_id, customerid). If you don't need the date in your application, you can just drop the field which would save you some storage.
MongoDB stores the datasets recently used in memory.
The index of a collection will always tried to be stored into RAM.
When an index is too large to fit into RAM, MongoDB must read the
index from disk, which is a much slower operation than reading from
RAM. Keep in mind an index fits into RAM when your server has RAM
available for the index combined with the rest of the working set.
Hope this helps.
Cheers dan
I think the issue with your thinking it that, somehow, you feel Node 1 would be faster than Node 2. Unless the hardware is drastically different then Node 1 and Node 2 would be accessed equally fast and thus reversing the strings would not help you out.
The main issue I see has to do with the number of customers in your system. This can lead to monotonic sharding wherein the last shard is the one always being hit and that can cause excessive splitting and migration. If you have a large number of customers then there is no issue, otherwise you might want to add another key on top of the customer id and date fields to more evenly divide up your content. I have heard of people using random identifiers, hashing the _id or using a GUID to overcome this issue.
I keep reading that using an ObjectId as the unique key makes sharding easier, but I haven't seen a relatively detailed explanation as to why that is. Could someone shed some light on this?
The reason I ask is that I want to use an english string (which will be unique obviously) as the unique key, but want to make sure that it won't tie my hands later on.
I've just recently been getting familiar with mongoDB myself so take this with a grain of salt but I suspect that sharding is probably more efficient when using ObjectId rather that your own key values because of the fact that part of the ObjectId will point out which machine or shard that the document was created on. The bottom of this page in the mongo docs explains what each portion of the ObjectId means.
I asked this question on Mongo user list and basically the reply was that it's OK to generate your own value of _id and it will not make sharding more difficult. For me sometimes it's necessary to have numeric values on _id like when I'm going to use them in url, so I'm generating my own _id in some collections.
ObjectId is designed to be globally unique. So, when used as a primary key and a new record is appended to the dataset without primary key value, then each shard can generate a new objectid and not worry about collisions with other shards. This somewhat simplifies life for everyone :)
Shard key does not have to be unique. We can't conclude that sharding a collection based on object id is always efficient .
Actually, ObjectID is probably a poor choice for a shard key.
From the docs (http://docs.mongodb.org/manual/core/sharded-cluster-internals/ the section on "Write Scaling"):
"[T]he most significant bits of [an ObjectID] represent a time stamp, which means that they increment in a regular and predictable pattern. [Therefore] all insert operations will be storing data into a single chunk, and therefore, a single shard. As a result, the write capacity of this shard will define the effective write capacity of the cluster."
In other words, because every OID sorts "bigger" than the one created immediately before it, an inserts that are keyed by OID will land on the same machine, and the write I/O capacity of that one machine will be the total I/O of your entire cluster. (This is true not just of OIDs, but any predictable key -- timestamps, autoincrementing numbers, etc.)
Contrariwise, if you chose a random string as your shard key, writes would tend to distribute evenly over the cluster, and your throughput would be the total I/O of the whole cluster.
(EDIT to be complete: with an OID shard key, as new records landed on the "rightmost" shard, the balancer would handle moving them elsewhere, so they would eventually end up on other machines. But that doesn't solve the I/O problem; it actually makes it worse.)