i am working on a migration from MS Sql to DynamoDB and i'm not sure what's the best hash key for my purpose. In MS SQL i've an item table where i store some product information for different customers, so actually the primary key are two columns customer_id and item_no. In application code i need to query specific items and all items for a customer id, so my first idea was to setup the customer id as hash key and the item no as range key. But is this the best concept in terms of partitioning? I need to import product data daily with 50.000-100.000 products for some larger customers and as far as i know it would be better to have a random hash key. Otherwise the import job will run on one partition only.
Can somebody give me a hint what's the best data model in this case?
Bye,
Peter
It sounds like you need item_no as the partition key, with customer_id as the sort key. Also, in order to query all items for a customer_id efficiently you will want to create a Global Secondary Index on customer_id.
This configuration should give you a good distribution while allowing you to run the queries you have specified.
You are on the right track, you should really be careful on how you are handling write operations as you are executing an import job in a daily basis. Also avoid adding indexes unnecessarily as they will only multiply your writing operations.
Using customer_id as hash key and item_no as range key will provide the best option not only to query but also to upload your data.
As you mentioned, randomization of your customer ids would be very helpful to optimize the use of resources and prevent a possibility of a hot partition. In your case, I would follow the exact example contained in the DynamoDB documentation:
[...] One way to increase the write throughput of this application
would be to randomize the writes across multiple partition key values.
Choose a random number from a fixed set (for example, 1 to 200) and
concatenate it as a suffix [...]
So when you are writing your customer information just randomly assign the suffix to your customer ids, make sure you distribute them evenly (e.g. CustomerXYZ.1, CustomerXYZ.2, ..., CustomerXYZ.200).
To read all of the items you would need to obtain all of the items for each suffix. For example, you would first issue a Query request for the partition key value CustomerXYZ.1, then another Query for CustomerXYZ.2, and so on through CustomerXYZ.200. Because you know the suffix range (on this case 1...200), you only need to query the records appending each suffix to the customer id.
Each query by the hash key CustomerXYZ.n should return a set of items (specified by the range key) from that specific customer, your application would need to merge the results from all of the Query requests.
This will for sure make your life harder to read the records (in terms of the additional requests needed), however, the benefits of optimized throughput and performance will pay off. Remember a hot partition will not only increase your overall financial cost, but will also impact drastically your performance.
If you have a well designed partition key your queries will always return very quickly with minimum cost.
Additionally, make sure your import job does not execute write operations grouped by customer, for example, instead of writing all items from a specific customer in series, sort the write operations so they are distributed across all customers. Even though your customers will be distributed by several partitions (due to the id randomization process), you are better off taking this additional safety measure to prevent a burst of write activity in a single partition. More details below:
From the 'Distribute Write Activity During Data Upload' section of the official DynamoDB documentation:
To fully utilize all of the throughput capacity that has been
provisioned for your tables, you need to distribute your workload
across your partition key values. In this case, by directing an uneven
amount of upload work toward items all with the same partition key
value, you may not be able to fully utilize all of the resources
DynamoDB has provisioned for your table. You can distribute your
upload work by uploading one item from each partition key value first.
Then you repeat the pattern for the next set of sort key values for
all the items until you upload all the data [...]
Source:
http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GuidelinesForTables.html
I hope that helps. Regards.
We have customer data that is sharded by a company ID. That is, no companies data would ever mix with another companies data so this was chosen as the distkey.
Should the company ID be the first column in the sortkey given that a node may contain several thousand companies? Or does the distkey already limit the data to a given company before it starts scanning?
Dist key does not affect the order in which rows are stored in each node/slice/block. Sort key (or natural order in the absence of such) defines the order.
If you expect frequent queries with company_id and you want to achieve maximum performance, make company_id the main sort key (COMPOUND or default, not just INTERLEAVED).
I'd also advise familiarising yourself with the SVL_QUERY_REPORT view. It can tell you whether full-scan was used (or range-restricted when using optimal sort keys), against which slices, and how many rows were actually scanned. Try different table layouts for the same data, and not only look at query times, but also confirm from this report that Redshift does what you expect it to do.
I am going to implement a notification system, and I am trying to figure out a good way to store notifications within a database. I have a web application that uses a PostgreSQL database, but a relational database does not seem ideal for this use case; I want to support various types of notifications, each including different data, though a subset of the data is common for all types of notifications. Therefore I was thinking that a NoSQL database is probably better than trying to normalize a schema in a relational database, as this would be quite tricky.
My application is hosted in Amazon Web Services (AWS), and I have been looking a bit at DynamoDB for storing the notifications. This is because it is managed, so I do not have to deal with the operations of it. Ideally, I'd like to have used MongoDB, but I'd really prefer not having to deal with the operations of the database myself. I have been trying to come up with a way to do what I want in DynamoDB, but I have been struggling, and therefore I have a few questions.
Suppose that I want to store the following data for each notification:
An ID
User ID of the receiver of the notification
Notification type
Timestamp
Whether or not it has been read/seen
Meta data about the notification/event (no querying necessary for this)
Now, I would like to be able to query for the most recent X notifications for a given user. Also, in another query, I'd like to fetch the number of unread notifications for a particular user. I am trying to figure out a way that I can index my table to be able to do this efficiently.
I can rule out simply having a hash primary key, as I would not be doing lookups by simply a hash key. I don't know if a "hash and range primary key" would help me here, as I don't know which attribute to put as the range key. Could I have a unique notification ID as the hash key and the user ID as the range key? Would that allow me to do lookups only by the range key, i.e. without providing the hash key? Then perhaps a secondary index could help me to sort by the timestamp, if this is even possible.
I also looked at global secondary indexes, but the problem with these are that when querying the index, DynamoDB can only return attributes that are projected into the index - and since I would want all attributes to be returned, then I would effectively have to duplicate all of my data, which seems rather ridiculous.
How can I index my notifications table to support my use case? Is it even possible, or do you have any other recommendations?
Motivation Note: When using a Cloud Storage like DynamoDB we have to be aware of the Storage Model because that will directly impact
your performance, scalability, and financial costs. It is different
than working with a local database because you pay not only for the
data that you store but also the operations that you perform against
the data. Deleting a record is a WRITE operation for example, so if
you don't have an efficient plan for clean up (and your case being
Time Series Data specially needs one), you will pay the price. Your
Data Model will not show problems when dealing with small data volume
but can definitely ruin your plans when you need to scale. That being
said, decisions like creating (or not) an index, defining proper
attributes for your keys, creating table segmentation, and etc will
make the entire difference down the road. Choosing DynamoDB (or more
generically speaking, a Key-Value store) as any other architectural
decision comes with a trade-off, you need to clearly understand
certain concepts about the Storage Model to be able to use the tool
efficiently, choosing the right keys is indeed important but only the
tip of the iceberg. For example, if you overlook the fact that you are
dealing with Time Series Data, no matter what primary keys or index
you define, your provisioned throughput will not be optimized because
it is spread throughout your entire table (and its partitions) and NOT
ONLY THE DATA THAT IS FREQUENTLY ACCESSED, meaning that unused data is
directly impacting your throughput just because it is part of the same
table. This leads to cases where the
ProvisionedThroughputExceededException is thrown "unexpectedly" when
you know for sure that your provisioned throughput should be enough for your
demand, however, the TABLE PARTITION that is being unevenly accessed
has reached its limits (more details here).
The post below has more details, but I wanted to give you some motivation to read through it and understand that although you can certainly find an easier solution for now, it might mean starting from the scratch in the near future when you hit a wall (the "wall" might come as high financial costs, limitations on performance and scalability, or a combination of all).
Q: Could I have a unique notification ID as the hash key and the user ID as the range key? Would that allow me to do lookups only by the range key, i.e. without providing the hash key?
A: DynamoDB is a Key-Value storage meaning that the most efficient queries use the entire Key (Hash or Hash-Range). Using the Scan operation to actually perform a query just because you don't have your Key is definitely a sign of deficiency in your Data Model in regards to your requirements. There are a few things to consider and many options to avoid this problem (more details below).
Now before moving on, I would suggest you reading this quick post to clearly understand the difference between Hash Key and Hash+Range Key:
DynamoDB: When to use what PK type?
Your case is a typical Time Series Data scenario where your records become obsolete as the time goes by. There are two main factors you need to be careful about:
Make sure your tables have even access patterns
If you put all your notifications in a single table and the most recent ones are accessed more frequently, your provisioned throughput will not be used efficiently.
You should group the most accessed items in a single table so the provisioned throughput can be properly adjusted for the required access. Additionally, make sure you properly define a Hash Key that will allow even distribution of your data across multiple partitions.
The obsolete data is deleted with the most efficient way (effort, performance and cost wise)
The documentation suggests segmenting the data in different tables so you can delete or backup the entire table once the records become obsolete (see more details below).
Here is the section from the documentation that explains best practices related to Time Series Data:
Understand Access Patterns for Time Series Data
For each table that you create, you specify the throughput
requirements. DynamoDB allocates and reserves resources to handle your
throughput requirements with sustained low latency. When you design
your application and tables, you should consider your application's
access pattern to make the most efficient use of your table's
resources.
Suppose you design a table to track customer behavior on your site,
such as URLs that they click. You might design the table with hash and
range type primary key with Customer ID as the hash attribute and
date/time as the range attribute. In this application, customer data
grows indefinitely over time; however, the applications might show
uneven access pattern across all the items in the table where the
latest customer data is more relevant and your application might
access the latest items more frequently and as time passes these items
are less accessed, eventually the older items are rarely accessed. If
this is a known access pattern, you could take it into consideration
when designing your table schema. Instead of storing all items in a
single table, you could use multiple tables to store these items. For
example, you could create tables to store monthly or weekly data. For
the table storing data from the latest month or week, where data
access rate is high, request higher throughput and for tables storing
older data, you could dial down the throughput and save on resources.
You can save on resources by storing "hot" items in one table with
higher throughput settings, and "cold" items in another table with
lower throughput settings. You can remove old items by simply deleting
the tables. You can optionally backup these tables to other storage
options such as Amazon Simple Storage Service (Amazon S3). Deleting an
entire table is significantly more efficient than removing items
one-by-one, which essentially doubles the write throughput as you do
as many delete operations as put operations.
Source:
http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GuidelinesForTables.html#GuidelinesForTables.TimeSeriesDataAccessPatterns
For example, You could have your tables segmented by month:
Notifications_April, Notifications_May, etc
Q: I would like to be able to query for the most recent X notifications for a given user.
A: I would suggest using the Query operation and querying using only the Hash Key (UserId) having the Range Key to sort the notifications by the Timestamp (Date and Time).
Hash Key: UserId
Range Key: Timestamp
Note: A better solution would be the Hash Key to not only have the UserId but also another concatenated information that you could calculate before querying to make sure your Hash Key grants you even access patterns to your data. For example, you can start to have hot partitions if notifications from specific users are more accessed than others... having an additional information in the Hash Key would mitigate this risk.
Q: I'd like to fetch the number of unread notifications for a particular user.
A: Create a Global Secondary Index as a Sparse Index having the UserId as the Hash Key and Unread as the Range Key.
Example:
Index Name: Notifications_April_Unread
Hash Key: UserId
Range Key : Unuread
When you query this index by Hash Key (UserId) you would automatically have all unread notifications with no unnecessary scans through notifications which are not relevant to this case. Keep in mind that the original Primary Key from the table is automatically projected into the index, so in case you need to get more information about the notification you can always resort to those attributes to perform a GetItem or BatchGetItem on the original table.
Note: You can explore the idea of using different attributes other than the 'Unread' flag, the important thing is to keep in mind that a Sparse Index can help you on this Use Case (more details below).
Detailed Explanation:
I would have a sparse index to make sure that you can query a reduced dataset to do the count. In your case you can have an attribute "unread" to flag if the notification was read or not, and use that attribute to create the Sparse Index. When the user reads the notification you simply remove that attribute from the notification so it doesn't show up in the index anymore. Here are some guidelines from the documentation that clearly apply to your scenario:
Take Advantage of Sparse Indexes
For any item in a table, DynamoDB will only write a corresponding
index entry if the index range key
attribute value is present in the item. If the range key attribute
does not appear in every table item, the index is said to be sparse.
[...]
To track open orders, you can create an index on CustomerId (hash) and
IsOpen (range). Only those orders in the table with IsOpen defined
will appear in the index. Your application can then quickly and
efficiently find the orders that are still open by querying the index.
If you had thousands of orders, for example, but only a small number
that are open, the application can query the index and return the
OrderId of each open order. Your application will perform
significantly fewer reads than it would take to scan the entire
CustomerOrders table. [...]
Instead of writing an arbitrary value into the IsOpen attribute, you
can use a different attribute that will result in a useful sort order
in the index. To do this, you can create an OrderOpenDate attribute
and set it to the date on which the order was placed (and still delete
the attribute once the order is fulfilled), and create the OpenOrders
index with the schema CustomerId (hash) and OrderOpenDate (range).
This way when you query your index, the items will be returned in a
more useful sort order.[...]
Such a query can be very efficient, because the number of items in the
index will be significantly fewer than the number of items in the
table. In addition, the fewer table attributes you project into the
index, the fewer read capacity units you will consume from the index.
Source:
http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GuidelinesForGSI.html#GuidelinesForGSI.SparseIndexes
Find below some references to the operations that you will need to programmatically create and delete tables:
Create Table
http://docs.aws.amazon.com/amazondynamodb/latest/APIReference/API_CreateTable.html
Delete Table
http://docs.aws.amazon.com/amazondynamodb/latest/APIReference/API_DeleteTable.html
I'm an active user of DynamoDB and here is what I would do... Firstly, I'm assuming that you need to access notifications individually (e.g. to mark them as read/seen), in addition to getting the latest notifications by user_id.
Table design:
NotificationsTable
id - Hash key
user_id
timestamp
...
UserNotificationsIndex (Global Secondary Index)
user_id - Hash key
timestamp - Range key
id
When you query the UserNotificationsIndex, you set the user_id of the user whose notifications you want and ScanIndexForward to false, and DynamoDB will return the notification ids for that user in reverse chronological order. You can optionally set a limit on how many results you want returned, or get a max of 1 MB.
With regards to projecting attributes, you'll either have to project the attributes you need into the index, or you can simply project the id and then write "hydrate" functionality in your code that does a look up on each ID and returns the specific fields that you need.
If you really don't like that, here is an alternate solution for you... Set your id as your timestamp. For example, I would use the # of milliseconds since a custom epoch (e.g. Jan 1, 2015). Here is an alternate table design:
NotificationsTable
user_id - Hash key
id/timestamp - Range key
Now you can query the NotificationsTable directly, setting the user_id appropriately and setting ScanIndexForward to false on the sort of the Range key. Of course, this assumes that you won't have a collision where a user gets 2 notifications in the same millisecond. This should be unlikely, but I don't know the scale of your system.
My main concern:
I have an existing table with huge data.It is having a clustered index.
My c++ process has a list of many keys with which it checks whether the key exists in the table,
and if yes, it will then check the row in the table and the new row are similar. if there is a change the new row is updated in the table.
In general there will less changes. But its huge data in the table.
S it means there will be lot of select queries but not many update queries.
What I would I like to achieve:
I just read about partitioning a table in sybase here.
I just wanted to know will this be helpful for me, as I read in the article it mentions about the insert queries only. But how can I improve my select query performance.
Could anyone please suggest what should I look for in this case?
Yes it will improve your query (read) performance so long as your query is based on the partition keys defined. Indexes can also be partitioned and it stands to reason that a smaller index will mean faster read performance.
For example if you had a query like select * from contacts where lastName = 'Smith' and you have partitioned your table index based on first letter of lastName, then the server only has to search one partition "S" to retrieve its results.
Be warned that partitioning your data can be difficult if you have a lot of different query profiles. Queries that do not include the index partition key (e.g. lastName) such as select * from staff where created > [some_date] will then have to hit every index partition in order to retrieve it's result set.
No one can tell you what you should/shouldn't do as it is very application specific and you will have to perform your own analysis. Before meddling with partitions, my advice is to ensure you have the correct indexes in place, they are being hit by your queries (i.e. no table scans), and your server is appropriately resourced (i.e got enough fast disk and RAM), and you have tuned your server caches to suit your queries.
It's my first time using a NoSQL database so I'm really confused. I'd really appreciate any help I can get.
I want to store data comprising announcements in my table. Essentially, each announcement has an ID, a date, and a text.
So for example, an announcement might have ID of 1, date of 2014/02/26, and text of "This is a sample announcement". Newer announcements always have a greater ID value than older announcements, since they are added to the table later.
There are two types of queries I want to run on this table:
I want to retrieve the text of the announcements sorted in order of date.
I want to retrieve the text and dates of the x most recent announcements (say, the 3 most recent announcements).
So I've set up the table with the following attributes:
ID (number) as primary key, and
date (string) as range
Is this appropriate for what my use cases? And if so, what kind of query/reads/requests/scans/whatever (I'm really confused about the terminology here too) should I be running to accomplish the two types of queries I want to make?
Any help will be very much appreciated. Thanks!
You are on the right track.
As far as sorting, DynamoDB will sort by the range key, so date will work but I'd recommend storing it as a number, perhaps milliseconds since the Unix epoch, rather than a String. This will make it trivial to get the announcements in ascending or descending order based on their created date.
See this answer for an overview of local vs global secondary indexes and what capabilities they provide: Optional secondary indexes in DynamoDB
As far as retrieving all items, you would need to perform a scan. Scans are not as efficient as queries, but since all of Dynamo is on SSD's they're still relatively quick. You don't get the single digit millisecond performance with a scan that you get with a query, so if there's a way to associate announcements with a user ID, you might get better performance than with a scan.
Note that you cannot modify the table schema (hash key, range key, and indexes) after you create the table. There are ways to manually migrate a table or import/export it, but the point is that you should think hard about current and future query requirements up front and design the table to support them. It's very easy to add or stop storing non-key or non-item attributes though, which provides nice flexibility.
Finally, try to avoid thinking of Dynamo as relational. With Dynamo, in a lot of cases you may well be better off de normalizing or duplicating some of the data in exchange for fast query performance.