As discussed in the previous article, a container can hold different types of items based on the type of API selected. These items of the container are further grouped into different subsets based on a partition key. So, the item’s of container gets partitioned into subsets based on a partition key and this helps in managing the performance of the container. Each item in a container will have a partition key and it determines to which logical partition the item belongs to. For example, let’s say a container having details of different departments of a company and each department has a DeptID. If there are 100 departments and if DeptID is selected as the partition key then there will be 100 different partitions in the container.
The data added to the container and the throughput provisioned on the container are distributed evenly across all the logical partitions of the container. As the data increases in the container, the logical partitions are created automatically. The maximum size of a logical partition is 10GB. The storage and throughput we select during the creation of the container affect the number of physical and logical partitions in a container.
The logical partitions of a container are mapped to physical partitions. These physical partitions consists of a set of replicas also called replica-set. These replica-sets hosts the cosmos DB instances. A single replica-set will have an instance of Cosmos DB.
The below picture shows how the logical partitions (LP1, LP2, LP3 and so on) of the above container C1 are mapped to different Physical partitions P1, P2 and P3. As we can see the physical partition P1 holds the two logical partitions LP1 and LP2. Same way P2 holds 3 logical partitions LP3, Lp4 and Lp5. The partition P3 holds the logical partition LP6.
These physical partitions are system controlled and we cannot manage these. But the logical partitions can be managed based on the partition key we select. Also, the efficient use of throughput provisioned on a container depends on the partition key selected. If the partition key selected fails to distribute the throughput evenly across the partitions of the container then the throughput provisioned on the container will not be used fully by the partitions. In such scenarios only a few partitions are utilized heavily and this type of partitions are called hot partitions. Each item within the partition will also have a unique itemid. The index of the item is formed by the combination of partition key and item ID.
Synthetic Partition Key:
As mentioned earlier choosing appropriate partition key plays a major role in distributing the data across partitions and in turn affects the throughput of the container. There might be situations where a particular field in a document cannot be a good partition key in such cases multiple fields can be concatenated and used as a partition key. This concept is similar to the concept of the composite index in SQL Server.
For example, in our department document example discussed at the beginning let’s say we have multiple fields like DeptID, DeptName, DeptLocation and so on. Here we can select DepID as the partition key field or we can use both DeptID and DeptLocation as the partition key.
DeptID
|
DepName
|
DepLocation
|
1
|
HR
|
Delhi
|
2
|
Payroll
|
Hyd
|
3
|
Security
|
Pune
|
4
|
Admin
|
Blr
|
5
|
Training
|
Chennai
|
If we select DeptID as partition key then partition key values would be 1,2,3,4 and so on. If we make synthetic partition key by concatenating DeptID andDepLocation then the synthetic partition key values would be “1-Delhi”, “2-Hyd”, “3-Pune” and so on.
Also, there is another option to add a random suffix to the partition key. Like a random number can be added as a suffix to the filed selected and used as a partition key. Like if DepLocation is suffixed with random numbers then the partition key values might be like “Delhi100”, “Hyd101”, “Pune102” and so on. The other option is suffixing the partition key with a pre-calculated value. The hash value of a field is calculated and the hash value will be suffixed to the partition key field. This pre-calculated value suffixing helps in distributing the data evenly across multiple partitions and even this leads to faster reads as well. This is one of the ways hot partitions can be reduced.
Other articles on CosmosDB:
Thanks VV!!
#CosmosDB, #containers, #partitions, #partitionkey, #nosql, #cloud
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