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.)
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.
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'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.
What is the Amazon-recommended way of changing the schema of a large table in a production DynamoDB?
Imagine a hypothetical case where we have a table Person, with primary hash key SSN. This table may contain 10 million items.
Now the news comes that due to the critical volume of identity thefts, the government of this hypothetical country has introduced another personal identification: Unique Personal Identifier, or UPI.
We have to add an UPI column and change the schema of the Person table, so that now the primary hash key is UPI. We want to support for some time both the current system, which uses SSN and the new system, which uses UPI, thus we need both these two columns to co-exist in the Person table.
What is the Amazon-recommended way to do this schema change?
There are a couple of approaches, but first you must understand that you cannot change the schema of an existing table. To get a different schema, you have to create a new table. You may be able to reuse your existing table, but the result would be the same as if you created a different table.
Lazy migration to the same table, without Streams. Every time you modify an entry in the Person table, create a new item in the Person table using UPI and not SSN as the value for the hash key, and delete the old item keyed at SSN. This assumes that UPI draws from a different range of values than SSN. If SSN looks like XXX-XX-XXXX, then as long as UPI has a different number of digits than SSN, then you will never have an overlap.
Lazy migration to the same table, using Streams. When streams becomes generally available, you will be able to turn on a Stream for your Person table. Create a stream with the NEW_AND_OLD_IMAGES stream view type, and whenever you detect a change to an item that adds a UPI to an existing person in the Person table, create a Lambda function that removes the person keyed at SSN and add a person with the same attributes keyed at UPI. This approach has race conditions that can be mitigated by adding an atomic counter-version attribute to the item and conditioning the DeleteItem call on the version attribute.
Preemptive (scripted) migration to a different table, using Streams. Run a script that scans your table and adds a unique UPI to each Person-item in the Person table. Create a stream on Person table with the NEW_AND_OLD_IMAGES stream view type and subscribe a lambda function to that stream that writes all the new Persons in a new Person_UPI table when the lambda function detects that a Person with a UPI was changed or when a Person had a UPI added. Mutations on the base table usually take hundreds of milliseconds to appear in a stream as stream records, so you can do a hot failover to the new Person_UPI table in your application. Reject requests for a few seconds, point your application to the Person_UPI table during that time, and re-enable requests.
DynamoDB streams enable us to migrate tables without any downtime. I've done this to great effective, and the steps I've followed are:
Create a new table (let us call this NewTable), with the desired key structure, LSIs, GSIs.
Enable DynamoDB Streams on the original table
Associate a Lambda to the Stream, which pushes the record into NewTable. (This Lambda should trim off the migration flag in Step 5)
[Optional] Create a GSI on the original table to speed up scanning items. Ensure this GSI only has attributes: Primary Key, and Migrated (See Step 5).
Scan the GSI created in the previous step (or entire table) and use the following Filter:
FilterExpression = "attribute_not_exists(Migrated)"
Update each item in the table with a migrate flag (ie: “Migrated”: { “S”: “0” }, which sends it to the DynamoDB Streams (using UpdateItem API, to ensure no data loss occurs).
NOTE: You may want to increase write capacity units on the table during the updates.
The Lambda will pick up all items, trim off the Migrated flag and push it into NewTable.
Once all items have been migrated, repoint the code to the new table
Remove original table, and Lambda function once happy all is good.
Following these steps should ensure you have no data loss and no downtime.
I've documented this on my blog, with code to assist:
https://www.abhayachauhan.com/2018/01/dynamodb-changing-table-schema/
I'm using a variant of Alexander's third approach. Again, you create a new table that will be updated as the old table is updated. The difference is that you use code in the existing service to write to both tables while you're transitioning instead of using a lambda function. You may have custom persistence code that you don't want to reproduce in a temporary lambda function and it's likely that you'll have to write the service code for this new table anyway. Depending on your architecture, you may even be able to switch to the new table without downtime.
However, the nice part about using a lambda function is that any load introduced by additional writes to the new table would be on the lambda, not the service.
If the changes involve changing the partition key, you can add a new GSI (global secondary index). Moreover, you can always add new columns/attributes to DynamoDB without needing to migrate tables.
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)