In every cosmos db repository example I've seen, the id/row key has been generated like this: {partitionKey}:{Guid.newGuid()}. I'm working on a web api where the user won't necessarily have any way of knowing what this random GUID is. But they will know the EmployeeId, ProjectId etc. of the respective object, so I'm wondering if there are any issues with using i.e. EmployeeId as both the partition key and Id?
There's nothing technically wrong with the approach of setting id and partition key the same however you will have just one document per partition and that's bad design IMHO as all your read queries will be cross-partition queries (e.g. listing all employees).
One approach could be to set the partition key as the type of the entity (Employee, Project etc.) and then set the id as the unique identifier of the entity (employee id, project id etc.).
To be honest, if you know the partition key AND the item id, you can do a Point read which is the fastest.
We used to also take the approach of using random guids for all item IDs, but this means you will always need to know this id and partition key. Sometimes a more functional key as the item ID makes more sense so have a good thought about it!
And remember, an item ID is not unique, the uniqueness is only within the partition key.
So you could have two items with the same item ID and different partition key.
Related
I have a DynamoDB table:
How shoul I filter entried in DB table where all keys are: access.role = "ADMIN"?
You would be best served by setting up an Global Index (GSI). You set the Partition Key equal to that attribute, and the Sort Key equal to some other attribute that you can guarantee will be unique. Then you use your SDK of choice or the Query option in the console, select the index, and query for partion_key = ADMIN
However. Be aware. Index's are a complete replication of the table. Dynamo is very good at this and relatively fast at doing so, but there is still the possibility that your index will be out of sync with the actual data. If you are not making the call against the index very often you are pretty much fine. If you are calling it very often, then you should restructure your table.
Dynamo is not an SQL. When setting up a dynamo schema you have to consider how you will access your data. your Access Patterns. You should design your data with your Partition Key as the data you will have when looking up (Ie: i always will have a user ID number) and your sort keys as the individual documents related to that PK (ie: a user has a document that is his profile data, a document that is his profile picture url, a document that is a list of his friends user numbers, a document that is ... ect)
Then you use Indexs for things like your question that you wont be doing very often.
So I wanted to fetch the last item/row of my dynamodb table but i am not finding resources. My primary key is id having series of incremented numbers such as 1,2,3... for each row respectively.
This is my function.
async function readMessage(){
const params = {
TableName: table,
};
return dynamo.getItem(params).promise();
}
I am not sure as to what i should be adding in my params.
DynamoDB has two types of primary keys:
Partition key – A simple primary key, composed of one attribute known as the partition key.
Partition key and sort key – Referred to as a composite primary key, this type of key is composed of two attributes. The first attribute is the partition key, and the second attribute is the sort key.
When fetching an item by partition key, you need to specify the exact partition key. You cannot fetch the max/min partition key.
Instead, you may want to create a sort key with a timestamp (or the ID if it's a sequential number) and use the sort key to fetch the last item.
Check out the AWS docs on Choosing the Right Partition Key for more info.
The proper way to design a table in DynamoDB is based on its expected access patterns; if this is something you need perhaps you should consider using this id as Sort Key instead of Primary Key and then query the table in descending order while also limiting the amount of items to 1.
If instead you don't want to change the schema of your items and you don't care about making at least two operations to do this you have two, not optimal options:
If none of your items ever gets deleted, just make a count first and use that information to know what's the latest item that was written.
Alternatively, if you could consider keeping a "special" record in your DynamoDB table that is basically a count that gets always incremented/written when one of your "other" items gets written. Upon retrieval you first retrieve the value of this special record and use this info to retrieve the actual one.
The combination of the partition key and sort key, makes the primary key of your item in the dynamoDB, so their combination must be unique, otherwise the item will be overwritten.
In almost all my use-cases, I select the primary key as an object attribute, like the brand, an email or a class and then, for the sort key I select the TimeStamp. So in this way, you always know the partition key, we need it to retrieve the values and then you can query your dynamoDB by making some filters by the sort key. For more extensive examples using Python, check the AWS page: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GettingStarted.Python.04.html, where it shows, how you can query your DynamoDB items.
There is also other ways to define the keys in your Dynamo and for that I advise you to check https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-sort-keys.html
A bit of context: I am trying to build an inventory to list my AWS resources in various accounts and I am planning to use DynamoDB to store the data. These will be the columns for my table: ResourceARN, ResourceName, ResourceType, StandardTag, IsDeleted, LastUpdateTime and ResourceCreationDate ( this field is available only for a few resource types like Ec2).
Question: I want to query my DDB table using account ID, resource type and tag name. I am stumped on choosing the primary key for the table. Since primary key should be unique and has to have 1:many relationship. Hence, I cannot use a combination of resourceType and account Id. Nor can I use resourceArn as my primary key since it is 1:1 relationship. Also, using the resourceARN as the sort key does not make sense to me. I understand that I can use a simple scan operation, but that is very costly and will take time if I add more data in my DDB.
I would appreciate any suggestions or guidance over the same.
Short answer
Partition key: Account ID
Sort key: <resource type>/<resource ID>
Rationale
It's a common pattern for a sort key to be a string concatenating multiple attributes. Since sort keys can be queried by prefix, you can leverage this in your queries:
Get all account resources: query all sort keys on the Account ID partition key
Get all EC2 instances of an account: query with partition key = <your account ID> and sort key begins_with('ec2-instance').
You may notice that ARNs follow such a hierarchy as well (what's probably not a coincidence). This would be effectively using a subset of the ARN as the sort key.
Some notes:
DynamoDB is about attributes as much as about columns. You don't need to include ResourceCreationDate in the records which don't have it, and doing so will save you space (see next point).
Attribute names count as storage for every record, which impacts cost and also throughput. It's common to use shorthand for names for this reason (rct instead of ResourceCreationTime for example).
You can use LSIs (Local Secondary Indexes) to order by creation and update times if you need this.
Ok, I have a table with primary partition key (Employee ID) and Sort Key (Poject ID). Now I want a list of all projects an employee works on. Also I want list of all employees working on a project. The relationship is many to many. I have created schema in AppSync (GraphQL). Appsync created the required queries and mutations for the type (EmployeeProjects). Now the ListEmployeeProjects takes a filter input with different attributes. My question is when I do the two searches on Employee ID or Project ID only, will it be a complete table scan? How efficient will that be. If it is a table scan, can I reduce the time complexity by creating indexes (GSI or LSI). The end product will have huge amount of data, so I cannot test the app with such data before hand. My project works fine, but I am worried about the problems that might arise later on with a lot of data. Can someone please help.
You don't need to (and should not) perform a Scan for this.
To get all of the projects an employee is working on, you just need to perform a Query on the base table, specifying employee ID as the partition key.
To get all of the employees on a project, you should create a GSI on the table. The partition key should be project ID and sort key should be employee ID. Then perform a Query on the GSI, using partition key of project ID.
In order to model this correctly you will probably want three tables
Employee Table
Project Table
Employee-Project reference table (i.e. just two attributes of employee ID and project ID)
I have set the partition key of one of my Cosmos DBs to /partition.
For example: We have a Chat document that contains a list of Subscribers, then we have ChatMessages that contain a text, a reference to the author and some other properties. Both documents have a partition property that contains the type 'chat' and the chats id.
Chat example:
{
"id" : "955f3eca-d28d-4f83-976a-f5ff26d0cf2c",
"name" : "SO questions",
"isChat" : true,
"partition" : "chat_955f3eca-d28d-4f83-976a-f5ff26d0cf2c",
"subscribers" : [
...
]
}
We then have Message documents like this:
{
"id" : "4d1c7b8c-bf89-47e0-83e1-a8cf0d71ce5a",
"authorId" : "some guid",
"isMessage" : true,
"partition" : "chat_955f3eca-d28d-4f83-976a-f5ff26d0cf2c",
"text" : "What should I do?"
}
It is now very convenient to return all messages for a specific chat, I just need to query all documents of the partition chat_955f3eca-d28d-4f83-976a-f5ff26d0cf2c with the property isMessage = true. All good...
But if I now want to query my db for a specific message by id, I usually just know the id, but not the partition and therefor have to run a slow crosspartition query. Which then led me to the question if I should not add the partitionKey to the message id so I can split the id when querying the db for a faster lookup. I saw that the _rid property of a document looks like a combination of the id of a db and the id of the collection and then a document specific id. What I mean by this is (simplified):
Chat.Id = "abc"
Chat.Partition = "chat_abc" //[type]_[chatId]
Message.Id = "chat_abc|123" //[Chat.Partition]|[Message.Id]
Message.Partition = chat_abc //[Chat.Partition]
Lets assume that I now want to get the Message document by the id, I just split the id by the | symbol and then query the document with the 1st part of the id as partition and the full id as the key.
Does that make sense? Are there better ways to do this? Should I just always also pass the partitionKey of a document along, not just it's id? Should I just use the _rid properties instead?
Any experience is highly appreciated!
UPDATE
I have found the following answer here:
Some applications encode partition key as part of the ID, e.g.
partition key would be customer ID, and ID = "customer_id.order_id",
so you can extract the partition key from the ID value.
I have further asked the cosmos team by email if this is a recommended pattern and post an answer, in case I get any.
Yes, your proposal to extract partition key from id (via a convention like a prefix/delimiter) makes sense. This is common among applications that have a single key and want to refactor it to use Cosmos DB from a different storage system.
If you're building your application from scratch, you should consider wiring the composite key (partition key + item key ("id")) through your API/application.
First, if you know your data (and index) size) will remain within the 10gb limit and you RU/sec limit is ok, then a fixed partition-less collection will bypass this problem. Probably OP has knowlingly made the decision that partitioning is required, but it is an important consideration to note for generalization purposes. If possible, KISS ;)
If partitioning is a must, then AFAIK you cannot avoid crosspartition split and its overhead unless you know the partition key.
Imho the OP suggestion of merging the duplicated partition key into id field is a rather ugly solution, because:
Name id implies it is unique key, partition key is not part of it or necessary for this key and its uniqueness. Anyone using this key upstream would incur the forced excess cost of longer key, blocked from using the simpler Guid type, etc.
It will become a mess should your partitioning key change in future.
The internal structure of merged id would not be intuitive without documentation - it's parts are not named and even if they look like to have a pattern new devs would not know for sure without finding external documentation to reliably understand what's going on.
Your data model does not require this duplication on semantic level, it would be for your application querying comfort and hence such hacks should belong to your application code, not data model. Such leaking concerns should be avoided if possible.
Data duplication within document would unnecessarily increase document size, bandwidth, etc. (may or may not be notable, depending on scale and usage). in-document duplication is necessary at times, but imho not necessarily in this case.
A better design would be to ensure the partition key is always present in logic context and could be passed along to lookups. If you don't have it available, then maybe you should refactor you application code (not data design) to explicitly pass around the chatId along with id where needed. That is WITHOUT merging them together into some opaque string format.
Also, I don't see a good way to use _rid for this as if I remember correctly, it did not contain any internal reference to a partition or partition key.
Disclaimer: I don't have any access or deep insight into internal CosmosDB index design or _rid logic on partitioned collections. I may have misunderstood how it works.