Are Azure CosmosDB indexes split by partition - azure-cosmosdb

I am sending some IoT events into Azure Cosmos DB. I am partitioning by device id and I am always querying by device id. I want to know if the automatically created indexes are separated by partition key. Specifically if I do query like
SELECT TOP 5 ... FROM events WHERE deviceId = X ORDER BY timeStamp DESC
Will it use the automatically created index on timeStamp and if so is it effective. Basically what I am asking is if there are separate indexes on timeStamp for each partition key (deviceId in my case) because otherwise the index will be relatively useless because the range will contain a lot of irrelevant data from other devices. If this was SQL Server I would create an index on deviceId followed by timeStamp but I am not sure how Cosmos DB works by default.

Indexes sit within the partition so yes.
For this query you have you should also create a composite index with DESC sort order for the best performance.

Related

Can I create with DynamoDB multiple tables with secondary index concurrencly?

I am confused by the API documentation of CreateTable from DynamoDB. I need to create multiple tables with a secondary index. From the API: https://sdk.amazonaws.com/java/api/latest/software/amazon/awssdk/services/dynamodb/DynamoDbClient.html#createTable-software.amazon.awssdk.services.dynamodb.model.CreateTableRequest-
If you want to create multiple tables with secondary indexes on them, you must create the tables sequentially. Only one table with secondary indexes can be in the CREATING state at any given time.
and
Up to 500 simultaneous table operations are allowed per account. These operations include CreateTable, UpdateTable, DeleteTable, UpdateTimeToLive, RestoreTableFromBackup, and RestoreTableToPointInTime.
The only exception is when you are creating a table with one or more secondary indexes. You can have up to 250 such requests running at a time;
Can I create now only one table with a secondary index or 250 at the same time?
If I create multiple tables sequential without waiting on active state is this already concurrency creation?
Must I wait on the active state for every table if I create multiple tables with secondary indexes?
An individual account can only be running one "Create Index" action at a time, no matter how many tables you have.
To understand this it may help to understand what an Index is. An Index is a complete copy of the table, but with a different partition and sort key. So if your original table has a PK of of userId and a sk of sort_key you could now create an index where the partition key is set to sort_key and the sort_key is now set to userId creating an inverted index (a common practice in Dynamo - remember Queries in Dynamo must know what the PK is, so if you have UserID you could access all data of a given User, or if you wanted all Users who have a particular tag, you may have an SK item on users that is something like TAG#ThisTag and then you wanted all users with ThisTag you could do a query against the inverted index with a pk = TAG#ThisTag and get back a list of UserIds.)
While the CreateIndex is being run on a given table, no other actions can be run on it - it wont accept changes to the data/configuration that would cause a fault/mismatch in the copying process. This is one of the reasons a given account is limited to only one create index operation at a time.
As a slight aside if I may - if you have a single account with multiple Dynamos all for the same product, you may want to rethink your database strategy. A single Dynamo Table can be used for many different storages if you set up your PK-SK as generic fields (ie: pk and sk as the attribute names) - No document inside your dynamo has to have the same attributes as any other. And when accessing data, each partition key is exactly as its named - a Partition of data that is all that is accessed when a query is made against that PK. (so if you have 100 items with PK of USER#1 and 100 items with a PK of USER#2 and you query against USER#1 you only access that 100 items - the rest are ignored by the Query and never ever touched - allowing you to in effect have multiple "tables" in a single DynamoDB Table by giving them different Partition Key prefixes.)

AWS DynamoDB Query based on non-primary keys

I'm new to AWS DynamoDB and wanted to clarify something. Is it possible to query a table and filter base on a non-primary key attribute. My table looks like the following
Store
Id: PrimaryKey
Name: simple string
Location: simple string
Now I want to query on the Name, but I think I have to give the key as well from what I know? Apart from that I can use the scan but then I will be loading all the data.
From the docs:
The Query operation finds items based on primary key values. You can query any table or secondary index that has a composite primary key (a partition key and a sort key).
DynamoDB requires queries to always use the partition key.
In your case your options are:
create a Global Secondary Index that uses Name as a primary key
use a Scan + Filter if the table is relatively small, or if you expect the result set will include the majority of the records in the table
There are few designs principals that you can follow while you are using DynamoDB. If you are coming from a relational background, you have already witnessed the query limitations from primary key attributes.
Design your tables, for querying and separating hot and cold data.
Create Indexes for Querying from Non Key attributes (You have two options, Global Secondary Index which you can define at any time and Local Secondary Index which you need to specify at table creation time).
With the Global Secondary Index you can promote any NonKey attribute as the Partition Key for the Index and select another attribute for Sort Key for querying. For Local Secondary Index, you can promote any Non Key attribute as the Sort Key keeping the same Partition Key.
Using Indexes for query is important also to improve the efficiency in using provisioned throughput.
Although having indexes consumes the read throughput from the table, it also saves read through put from in a way that, if you project the right amount of attributes to read, it can give a huge benefit in reading. Check the following example.
Lets say you have a DynamoDB table that has items of 40KB. If you read directly from the table to list 10 items, it consumes 100 Read Throughput Units (For one item 10 Units since one unit can read 4KB and multiply it by 10). If you have an index defined just to project the attributes needed to list which will be having 4KB per item, then it will be consuming only 10 Read Throughput Units(One Unit per item) which makes a huge difference in terms of cost.
With DynamoDB its really important how you define Indexes to optimize for Querying not only from Query capability but also in terms of throughput.
You can not query based non-primary key attribute in Dynamo Db.
If you wanted to still do that you can do it using scan query,but scan is costly operation in DyanmoDB and if table is large, then it will affect performance and not recommended because it will scan each item in table and AWS cost you for all item it scan for that query.
There are two ways to achieve it
Keep Store Id as your PrimaryKey/ Partaion key of Dyanmo DB table and add Name/Location as sort Key (only one as Dyanmo DB accept only one Attribute as sort key by design.
Create Global Secondary Indexes for Querying from Non Key attributes which you are more frequenly required.
There are 3 ways to created GSI in Dyanamo DB, In your case select GSI with option INCLUDE and add Name , Location and store ID in Idex.
KEYS_ONLY – Each item in the index consists only of the table partition key and sort key values, plus the index key values. The KEYS_ONLY option results in the smallest possible secondary index.
INCLUDE – In addition to the attributes described in KEYS_ONLY, the secondary index will include other non-key attributes that you specify.
ALL – The secondary index includes all of the attributes from the source table. Because all of the table data is duplicated in the index, an ALL projection results in the largest possible secondary index.

Dynamodb query expression

Team,
I have a dynamodb with a given hashkey (userid) and sort key (ages). Lets say if we want to retrieve the elements as "per each hashkey(userid), smallest age" output, what would be the query and filter expression for the dynamo query.
Thanks!
I don't think you can do it in a query. You would need to do full table scan. If you have a list of hash keys somewhere, then you can do N queries (in parallel) instead.
[Update] Here is another possible approach:
Maintain a second table, where you have just a hash key (userID). This table will contain record with the smallest age for given user. To achieve that, make sure that every time you update main table you also update second one if new age is less than current age in the second table. You can use conditional update for that. Update can either be done by application itself, or you can have AWS lambda listening to dynamoDB stream. Now if you need smallest age for each use, you still do full table scan of the second table, but this scan will only read relevant records, to it will be optimal.
There are two ways to achieve that:
If you don't need to get this data in realtime you can export your data into a other AWS systems, like EMR or Redshift and perform complex analytics queries there. With this you can write SQL expressions using joins and group by operators.
You can even perform EMR Hive queries on DynamoDB data, but they perform scans, so it's not very cost efficient.
Another option is use DynamoDB streams. You can maintain a separate table that stores:
Table: MinAges
UserId - primary key
MinAge - regular numeric attribute
On every update/delete/insert of an original query you can query minimum age for an updated user and store into the MinAges table
Another option is to write something like this:
storeNewAge(userId, newAge)
def smallestAge = getSmallestAgeFor(userId)
storeSmallestAge(userId, smallestAge)
But since DynamoDB does not has native transactions support it's dangerous to run code like that, since you may end up with inconsistent data. You can use DynamoDB transactions library, but these transactions are expensive. While if you are using streams you will have consistent data, at a very low price.
You can do it using ScanIndexForward
YourEntity requestEntity = new YourEntity();
requestEntity.setHashKey(hashkey);
DynamoDBQueryExpression<YourEntity> queryExpression = new DynamoDBQueryExpression<YourEntity>()
.withHashKeyValues(requestEntity)
.withConsistentRead(false);
equeryExpression.setIndexName(IndexName); // if you are using any index
queryExpression.setScanIndexForward(false);
queryExpression.setLimit(1);

Is partition key needed in queries even though JSON is indexed

I'm planning on using Cosmos Db (Document Db) and I'm trying to understand how the queries, indexing and partitions relate to each other.
How to partition and scale in Azure Cosmos Db talks about the partition key and other documentation indicates that partition key + id = unique id for the document. But then SQL Query and SQL syntax in Azure Cosmos Db says it provides automatic indexing of JSON documents without requiring explicit schema or creation of secondary indexes.
I understand that partition key is important for scalability and how data is stored. But if we think about searching is the partition key kind of like extra filter/where clause? All the documents are indexed so I can execute query like:
SELECT *
FROM Families
WHERE Families.address.state = "NY"
Should I still specify the partition key or indicate some how that cross partition queries are allowed when using this SQL query syntax?
Your first link gives the answer for this:
For partitioned collections, you can use PartitionKey to run the query against a single partition (though Cosmos DB can automatically extract this from the query text), and EnableCrossPartitionQuery to run queries that may need to be run against multiple partitions.
So, yes, you either need to specify the WHERE clause which will make query run against a single partition, or set EnableCrossPartitionQuery to true in query options.
You don't have to do that anymore, EnableCrossPartitionQuery is set to true by default nowadays. This means Cosmos won't complain if you don't skip the partition key in your query.
More info here.
You don't need to specify a partition key to the query. Recent version enabled cross partition queries by default

How to design DynamoDB table to facilitate searching by time ranges, and deleting by unique ID

I'm new to DynamoDB - I already have an application where the data gets inserted, but I'm getting stuck on extracting the data.
Requirement:
There must be a unique table per customer
Insert documents into the table (each doc has a unique ID and a timestamp)
Get X number of documents based on timestamp (ordered ascending)
Delete individual documents based on unique ID
So far I have created a table with composite key (S:id, N:timestamp). However when I come to query it, I realise that since my id is unique, because I can't do a wildcard search on ID I won't be able to extract a range of items...
So, how should I design my table to satisfy this scenario?
Edit: Here's what I'm thinking:
Primary index will be composite: (s:customer_id, n:timestamp) where customer ID will be the same within a table. This will enable me to extact data based on time range.
Secondary index will be hash (s: unique_doc_id) whereby I will be able to delete items using this index.
Does this sound like the correct solution? Thank you in advance.
You can satisfy the requirements like this:
Your primary key will be h:customer_id and r:unique_id. This makes sure all the elements in the table have different keys.
You will also have an attribute for timestamp and will have a Local Secondary Index on it.
You will use the LSI to do requirement 3 and batchWrite API call to do batch delete for requirement 4.
This solution doesn't require (1) - all the customers can stay in the same table (Heads up - There is a limit-before-contact-us of 256 tables per account)

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