I am unable to find any documentation mentioning how are cosmos db indexes organized per the number of physical partitions. If i have my logical partition split into multiple physical partitions and assuming i am not including a partition key in the filter and have created an index on the field i am querying with.
What would the behavior be in terms of index. Does cosmos create an individual index per physical partition or a centrally maintained global index?
Can someone please explain what the behavior could be in such a case or point to some documentation in azure which explains how this would work.
A physical partition is simply a compute and storage node on which your data resides. A partition key within your WHERE clause routes the query to the partition where that data resides. Indexes reside within each partition and index the data for that partition only. Partitions are share nothing. In addition to routing, partition keys must also be included in your index policy when used in queries.
A query without a partition key in the filter will fan out to every partition within a container. At small scales (< 10K RU/s or < 50GB) this isn't much of an issue because the data is all located on a single physical partition. However, as the amount of storage and throughput grows, this query will likely become increasingly more expensive with greater latency. In short, the query will not scale. This is because as the size grows, so does the number of physical partitions that must be contacted to serve the same query.
More information here, Tuning query performance with Azure Cosmos DB and here, Indexing Overview
Perhaps this ms learn article provides the information you are looking for or this one for more details.
A Logical partition is mapped to only one physical partition;
Physical partitions are an internal implementation of the system and they are entirely managed by Azure Cosmos DB.
Azure Cosmos DB will automatically create new physical partitions by splitting existing ones
Kind regards
Related
I have a container that stores ~5000 documents. Each document is not very large. The most frequent query is just to select everything in this container (so that the frontend can display it in a nice table client-side). Each document has a unique ID. I was using this as the partition key (/id) for the container but I have read that querying data like this is more efficient in terms of time and RU/s when all the data comes from the same partition as I can avoid cross-partition queries.
Can I create a container without a partition key? Or a container that only has one partition? Will I have to add a property to every document that is the same value to force this or is there an easier way?
The number of partitions of a container is defined by the provisioned RU and data size: https://learn.microsoft.com/azure/cosmos-db/partitioning-overview#physical-partitions
So, if you create a container with less than 10K RU and keep the data size small (<50GB), it should be a single physical partition.
If you use a single value for your Partition Key, you will hit the data cap: https://learn.microsoft.com/azure/cosmos-db/sql/troubleshoot-forbidden#partition-key-exceeding-storage because your database simply won't be able to scale.
Looking at the no of documents in your container (~5000), it would ideally land in a single physical partition unless you have huge amount of RU requirement above 10,000 RU's. Assuming you have an RU config of less than 10,000 RU/S, this would be in a single physical partition >You can confirm this by looking at the metric(classic) option from the left-hand blade in the portal
Does anyone know how to check what kind of data is on a certain partition in Azure Cosmos/MongoDB ?
I have one partition exceeding the storage limit, but I can't figure out why. I have one collection, with around 70 partitions. All of the partitions are 3.5GB or less, but one partition is 10GB, causing problems as it exceeds the maximum amount of data.
Yesterday, I removed for about 15% of the data for that partition, but it still claims to be on 10GB.
How can I check which documents are residents of the particular partition?
Rens Groenveld,firstly,I'd say sorry for the misunderstanding in my comment. After observing your screenshot, combining this blog, the partition key(in 2nd pic) is for logical partition and the partition name(in 3rd pic) is for physical partition.
You specify the partition key to create a logicalpartition that
guarantees to keep items with the same hash of the key together.
Cosmos DB manages the physical partitions based on needs. In the
portal, you can see that although we have a few dozen partition keys,
there are only a handful of partitions.
You could know the differences between logical partition and physical partition from this official document.
Unlike logical partitions, physical partitions are an internal
implementation of the system. You can't control their size, placement,
the count, or the mapping between the logical partitions and the
physical partitions. However, you can control the number of logical
partitions and the distribution of data and throughput by choosing the
right partition key.
Back to your issue, your logical partitioned data is keep together logically, cosmos db would balance them physically which we can't get involved. Data for the same logical partition maybe not reside on the same physical partition. I assumed that's the reason you remove the data does't save the size. I suggest you getting touch with Azure Cosmos DB Team to see what they can do with your physical partition.
I'm new to Azure Cosmos DB, but I want to have a vivid understanding of:
What is the partition key?
My understanding is shallow for now -> items with the same partition key will go to the same partition for storage, which could better load balancing when the system grows bigger.
How to decide on a good partition key?
Could somebody please provide an example?
Thanks a lot!
You have to choose your partition based on your workload. They can be classified into two.
Read Heavy
Write Heavy
Read heavy workloads are where the data is read more than it has been written, like the product catalog, where the insert/update frequency of the catalogs is less, and people browsing the product is more.
Write Heavy workloads are the ones where the data is written more than it is read. Common scenarios are IoT devices sending multiple data from multiple sensors. You will be writing lots of data to Cosmos DB because you may get data every second.
For read-heavy workload choose the partition key, where the property is used in the filter query. The product example will be the product id, which will be used mostly to fetch the data when the user wants to read the information and browse its reviews.
For Write-heavy workload choose the partition key, where the property is more unique. For example, in the IoT Scenario, use the partition key such as deviceid_signaldatetime, which is concatenating the device-id that sends the signal, and DateTime of the signal has more uniqueness.
1.What is the partition key?
In azure cosmos db , there are two partitions: physical partition and logical partition
A.Physical partition is a fixed amount of reserved SSD-backed storage combined with variable amount of compute resources.
B.Logical partition is a partition within a physical partition that stores all the data associated with a single partition key value.
I think the partiton key you mentioned is the logical partition key.The partition key acts as a logical partition for your data and provides Azure Cosmos DB with a natural boundary for distributing data across physical partitions.More details, you could refer to How does partitioning work.
2.How to decide a good partition key? Could somebody please provide an example?
You need consider to pick a property name that has a wide range of values and has even access patterns.An ideal partition key is one that appears frequently as a filter in your queries and has sufficient cardinality to ensure your solution is scalable.
For example, your data has fields named id and color and you query the color as filter more frequently.You need to pick the color not id for partition key which is more efficient for your query performance. Because every item has different id but maybe has same color.It has wide range. Also if you add a color,the partition key is scalable.
More details ,please read the Partition and scale in Azure Cosmos DB.
Hope it helps you.
I'm setting up our first Azure Cosmos DB - I will be importing into the first collection, the data from a table in one of our SQL Server databases. In setting up the collection, I'm having trouble understanding the meaning and the requirements around the partition key, which I specifically have to name while setting up this initial collection.
I've read the documentation here: (https://learn.microsoft.com/en-us/azure/cosmos-db/documentdb-partition-data) and still am unsure how to proceed with the naming convention of this partition key.
Can someone help me understand how I should be thinking in naming this partition key? See the screenshot below for the field I'm trying to fill in.
In case it helps, the table I'm importing consists of 7 columns, including a unique primary key, a column of unstructured text, a column of URL's and several other secondary identifiers for that record's URL. Not sure if any of that information has any bearing on how I should name my Partition Key.
EDIT: I've added a screenshot of several records from the table from which I'm importing, per request from #Porschiey.
Honestly the video here* was a MAJOR help to understanding partitioning in CosmosDb.
But, in a nutshell:
The PartitionKey is a property that will exist on every single object that is best used to group similar objects together.
Good examples include Location (like City), Customer Id, Team, and more. Naturally, it wildly depends on your solution; so perhaps if you were to post what your object looks like we could recommend a good partition key.
EDIT: Should be noted that PartitionKey isn't required for collections under 10GB. (thanks David Makogon)
* The video used to live on this MS docs page entitled, "Partitioning and horizontal scaling in Azure Cosmos DB", but has since been removed. A direct link has been provided, above.
Partition key acts as a logical partition.
Now, what is a logical partition, you may ask? A logical partition may vary upon your requirements; suppose you have data that can be categorized on the basis of your customers, for this customer "Id" will act as a logical partition and info for the users will be placed according to their customer Id.
What effect does this have on the query?
While querying you would put your partition key as feed options and won't include it in your filter.
e.g: If your query was
SELECT * FROM T WHERE T.CustomerId= 'CustomerId';
It will be Now
var options = new FeedOptions{ PartitionKey = new PartitionKey(CustomerId)};
var query = _client.CreateDocumentQuery(CollectionUri,$"SELECT * FROM T",options).AsDocumentQuery();
I've put together a detailed article here Azure Cosmos DB. Partitioning.
What's logical partition?
Cosmos DB designed to scale horizontally based on the distribution of data between Physical Partitions (PP) (think of it as separately deployable underlaying self-sufficient node) and logical partition - bucket of documents with same characteristic (partition key) which is supposed to be stored fully on the same PP. So LP can't have part of the data on PP1 and another on PP2.
There are two main limitation on Physical Partitions:
Max throughput: 10k RUs
Max data size (sum of sizes of all LPs stored in this PP): 50GB
Logical partition has one - 20GB limit in size.
NOTE: Since initial releases of Cosmos DB size limits grown and I won't be surprised that soon size limitations might increase.
How to select right partition key for my container?
Based on the Microsoft recommendation for maintainable data growth you should select partition key with highest cardinality (like Id of the document or a composite field). For the main reason:
Spread request unit (RU) consumption and data storage evenly across all logical partitions. This ensures even RU consumption and storage distribution across your physical partitions.
It is critical to analyze application data consumption pattern when considering right partition key. In a very rare scenarios larger partitions might work though in the same time such solutions should implement data archiving to maintain DB size from a get-go (see example below explaining why). Otherwise you should be ready to increasing operational costs just to maintain same DB performance and potential PP data skew, unexpected "splits" and "hot" partitions.
Having very granular and small partitioning strategy will lead to an RU overhead (definitely not multiplication of RUs but rather couple additional RUs per request) in consumption of data distributed between number of physical partitions (PPs) but it will be neglectable comparing to issues occurring when data starts growing beyond 50-, 100-, 150GB.
Why large partitions are a terrible choice in most cases even though documentation says "select whatever works best for you"
Main reason is that Cosmos DB is designed to scale horizontally and provisioned throughput per PP is limited to the [total provisioned per container (or DB)] / [number of PP].
Once PP split occurs due to exceeding 50GB size your max throughput for existing PPs as well as two newly created PPs will be lower then it was before split.
So imagine following scenario (consider days as a measure of time between actions):
You've created container with provisioned 10k RUs and CustomerId partition key (which will generate one underlying PP1). Maximum throughput per PP is 10k/1 = 10k RUs
Gradually adding data to container you end-up with 3 big customers with C1[10GB], C2[20GB] and C3[10GB] of invoices
When another customer was onboarded to the system with C4[15GB] of data Cosmos DB will have to split PP1 data into two newly created PP2 (30GB) and PP3 (25GB). Maximum throughput per PP is 10k/2 = 5k RUs
Two more customers C5[10GB] C6[15GB] were added to the system and both ended-up in PP2 which lead to another split -> PP4 (20GB) and PP5 (35GB). Maximum throughput per PP is now 10k/3 = 3.333k RUs
IMPORTANT: As a result on [Day 2] C1 data was queried with up to 10k RUs
but on [Day 4] with only max to 3.333k RUs which directly impacts execution time of your query
This is a main thing to remember when designing partition keys in current version of Cosmos DB (12.03.21).
CosmosDB can be used to store any limit of data. How it does in the back end is using partition key. Is it the same as Primary key? - NO
Primary Key: Uniquely identifies the data
Partition key helps in sharding of data(For example one partition for city New York when city is a partition key).
Partitions have a limit of 10GB and the better we spread the data across partitions, the more we can use it. Though it will eventually need more connections to get data from all partitions. Example: Getting data from same partition in a query will be always faster then getting data from multiple partitions.
Partition Key is used for sharding, it acts as a logical partition for your data, and provides Cosmos DB with a natural boundary for distributing data across partitions.
You can read more about it here: https://learn.microsoft.com/en-us/azure/cosmos-db/partition-data
Each partition on a table can store up to 10GB (and a single table can store as many document schema types as you like). You have to choose your partition key though such that all the documents that get stored against that key (so fall into that partition) are under that 10GB limit.
I'm thinking about this too right now - so should the partition key be a date range of some type? In that case, it would really depend on how much data is getting stored in a period of time.
You are defining a logical partition.
Underneath, physically the data is split into physical partitions by Azure.
Ideally a partitionKey should be a primary Key, or a field with high cardinality to ensure proper distribution, with the self generated id field within that partition also set to the primary key, that will help with documentFetchById much faster.
You cannot change a partitionKey once container is created.
Looking at the dataset, captureId is a good candidate for partitionKey, with id set manually to this field, and not an auto generated cosmos one.
There is documentation available from Microsoft about partition keys. According to me you need to check the queries or operations that you plan to perform with cosmos DB. Are they read-heavy or write-heavy? if read heavy it is ideal to choose a partition key in the where clause that will be used in the query, if it is a write heavy operation then look for a key which has high cardinality
Always point reads /writes are better since it consumes way less RU's than running other queries
In Azure Cosmos DB partinioned collection, does each partition has any size limit?
As per this old document, they have a size limit of 10 GB. Is that the same now also?
https://azure.microsoft.com/en-in/blog/10-things-to-know-about-documentdb-partitioned-collections/
Regards,
Karthikeyan V.
A partitioned collection has individual 10GB partition spaces. For a given partition key, you cannot exceed 10GB of data. This has not changed.
You'll need to pick a partition key which distributes your data across many partitions, vs creating "hot" partitions which could fill up (where you'd then get an error when attempting to write content).
There are two type of collection
Single Partition Collection (10GB and 10,000 RU/s)
Partitioned Collection (250 GB and 250,000 RU/s)- you can increase the limit as needed after contacting azure team.
For partitioned collection you mush have to specify a partition key based on your query filter for better read performance and if you will not mention it will be by default single partition collection.
Note: Collection is a logical space and it can span across multiple node(hence quorum) in background based on the RU and other param, in short it's a PAAS and the infra handling is automated behind the screen, you will not have much control over it.
More info here:
Partitioning and horizontal scaling in Azure Cosmos DB