I have a DynamoDB-based web application that uses DynamoDB to store my large JSON objects and perform simple CRUD operations on them via a web API. I would like to add a new table that acts like a categorization of these values. The user should be able to select from a selection box which category the object belongs to. If a desirable category does not exist, the user should be able to create a new category specifying a name which will be available to other objects in the future.
It is critical to the application that every one of these categories be given a integer ID that increments starting the first at 1. These numbers that are auto generated will turn into reproducible serial numbers for back end reports that will not use the user-visible text name.
So I would like to have a simple API available from the web fronted that allows me to:
A) GET /category : produces { int : string, ... } of all categories mapped to an ID
B) PUSH /category : accepts string and stores the string to the next integer
Here are some ideas for how to handle this kind of project.
Store it in DynamoDB with integer indexes. This leaves has some benefits but it leaves a lot to be desired. Firstly, there's no auto incrementing ID in DynamoDB, but I could definitely get the state of the table, create a new ID, and store the result. This might have issues with consistency and race conditions but there's probably a way to achieve this safely. It might, however, be a big anti pattern to use DynamoDB this way.
Store it in DynamoDB as one object in a table with some random index. Just store the mapping as a JSON object. This really forgets the notion of tables in DynamoDB and uses it as a simple file. It might also run into some issues with race conditions.
Use AWS ElasticCache to have a Redis key value store. This might be "the right" decision but the downside is that ElasticCache is an always on DB offering where you pay per hour. For a low-traffic web site like mine I'd be paying minumum $12/mo I think and I would really like for this to be pay per access/update due to the low volume. I'm not sure there's an auto increment feature for Redis built in the way I'd need it. But it's pretty trivial to make a trasaction that gets the length of the table, adds one, and stores a new value. Race conditions are easily avoid with this solution.
Use a SQL database like AWS Aurora or MYSQL. Well this has the same upsides as Redis, but it's also more overkill than Redis is, and also it costs a lot more and it's still always on.
Run my own in memory web service or MongoDB etc... still you're paying for constant containers running. Writing my own thing is obviously silly but I'm sure there are services that match this issue perfectly but they'd all require a constant container to run.
Is there a food way to just store a simple list, or integer mapping like this that doesn't cost a constant monthly cost? Is there a better way to do this with DynamoDB?
Store the maxCounterValue as an item in DyanamoDB.
For the PUSH /category, perform the following:
Get the current maxCounterValue.
TransactWrite:
Put the category name and id into a new item with id = maxCounterValue + 1.
Update the maxCounterValue +1, add a ConditionExpression to check that maxCounterValue = :valueFromGetOperation.
If TransactWrite fails, start at 1 again, try X more times
Related
I've included some links along with our approaches to other answers, which seem to be the most optimal on the web right now.
Our records need to be categorized (eg. "horror", "thriller", "tv"), and randomly accessible both in specific categories and across all/some categories. We generally need to access about 20 - 100 items at a time. We also have a smallish number of categories (less than 100).
We write to the database for uploading/removing content, although this is done in batches and does not need to be real time.
We have tried two different approaches, with two different data structures.
Approach 1
AWS DynamoDB - Pick a record/item randomly?
Help selecting nth record in query.
In short, using the category as a hash key, and a UUID as the sort key. Generate a random UUID, query Dynamo using greater than or less than, and limit to 1. This is even suggested by an AWS employee in the second link. (We've also tried increasing the limit to the number of items we need, but this increases the probability of the query failing the first time around).
Issues with this approach:
First query can fail if it is greater than/less than any of the UUIDs
Querying on any specific category will cause throttling at scale (Small number of partitions)
We've also considered adding a suffix to each category to artificially increase the number of partitions we have, as pointed out in the following link.
AWS Database Blog
Choosing the Right DynamoDB Partition Key
Approach 2
Amazon Web Services: How do we get random item from the dynamoDb's table?
Doing something similar to this, where we concatenate the category with a sequential number, and use this as the hash key. e.g. horror-000001.
By knowing the number of records in each category, we're able to perform random queries across our entire data set, while also avoiding hot partitions/keys.
Issues with this approach
We need a secondary data structure to manage the sequential counts across each category
Writing (especially deleting) is significantly more complex, although this doesn't need to happen in real time.
Conclusion
Both approaches solve our main use case of random queries on category/categories, but the cons they offer are really deterring us from using them. We're leaning more towards approach #1 using suffixes to solve the hot partitioning issue, although we would need the additional retry logic for failed queries.
Is there a better way of approaching this problem? Specifically looking for solutions capable of scaling well (No scan), without requiring extra resources be implemented. #1 fits the bill, but needing to manage suffixes and failed attempts really deters us from using it, especially when it is being called inside a lambda (billed for time used).
Thanks!
Follow Up
After more research and testing, my team has decided to move towards MySQL hosted on RDS for these tables. We learned that this is one of the few use cases were DynamoDB does not fit, and requires rewriting your use case to fit the DB (Bad).
We felt that the extra complexity required to integrate random sampling on DynamoDB wasn't worth it, and we were unable to come up with any comparable solutions. We are, however, sticking with DynamoDB for our tables that do not need random accessibility due to the price and response times.
For anyone wondering why we chose MySQL, it was largely due to the Nodejs library available, great online resources (which DynamoDB definitely lacks), easy integration via RDS with our Lambdas, and the option to migrate to Amazons Aurora database.
We also looked at PostgreSQL, but we weren't as happy with the client library or admin tools, and we believe that MySQL will suit our needs for these tables.
If anybody has anything else they'd like to add or a specific question please leave a comment or send me a message!
This was too long for a comment, and I guess it's pretty much a full fledged answer now.
Approach 2
I've found that my typical time to get a single item from dynamodb to a host in the same region is <10ms. As long as you're okay with at most 1-2 extra calls, you can quite easily implement approach 2.
If you use a keys only GSI where the category is your hash key and the primary key of the table is your range key, you can quickly find the largest numbered single item within a category.
When you add a new item, find the largest number for that category from the GSI and then write the new item to the table with sequence number n+1.
When you delete, find the item with the largest sequence number for that category from the GSI, overwrite the item you are deleting, and then delete the now duplicated item from its position at the highest sequence number.
To randomly get an item, query the GSI to find the highest numbered item in the category, and then randomly pick a number since you now know the valid range.
Approach 1
I'm not sure exactly what you mean when you say "without requiring extra resources to be implemented". If you're okay with using a managed resource (no dev work to implement), you can also make Approach 1 work by putting a DAX cluster in front of your dynamodb table. Then you can query to your heart's content without really worrying about hot partitions. (Though the caching layer means that new/deleted items won't be reflected right away.)
Situation
I'm using multiple storage databases as attachments to one central "manager" DB.
The storage tables share one pseudo-AUTOINCREMENT index across all storage databases.
I need to iterate over the shared index frequently.
The final number and names of storage tables are not known on storage DB creation.
On some signal, a then-given range of entries will be deleted.
It is vital that no insertion fails and no entry gets deleted before its signal.
Energy outage is possible, data loss in this case is hardly, if ever, tolerable. Any solutions that may cause this (in-memory databases etc) are not viable.
Database access is currently controlled using strands. This takes care of sequential access.
Due to the high frequency of INSERT transactions, I must trigger WAL checkpoints manually. I've seen journals of up to 2GB in size otherwise.
Current solution
I'm inserting datasets using parameter binding to a precreated statement.
INSERT INTO datatable VALUES (:idx, ...);
Doing that, I remember the start and end index. Next, I bind it to an insert statement into the registry table:
INSERT INTO regtable VALUES (:idx, datatable);
My query determines the datasets to return like this:
SELECT MIN(rowid), MAX(rowid), tablename
FROM (SELECT rowid,tablename FROM entryreg LIMIT 30000)
GROUP BY tablename;
After that, I query
SELECT * FROM datatable WHERE rowid >= :minid AND rowid <= :maxid;
where I use predefined statements for each datatable and bind both variables to the first query's results.
This is too slow. As soon as I create the registry table, my insertions slow down so much I can't meet benchmark speed.
Possible Solutions
There are several other ways I can imagine it can be done:
Create a view of all indices as a UNION or OUTER JOIN of all table indices. This can't be done persistently on attached databases.
Create triggers for INSERT/REMOVE on table creation that fill a registry table. This can't be done persistently on attached databases.
Create a trigger for CREATE TABLE on database creation that will create the triggers described above. Requires user functions.
Questions
Now, before I go and add user functions (something I've never done before), I'd like some advice if this has any chances of solving my performance issues.
Assuming I create the databases using a separate connection before attaching them. Can I create views and/or triggers on the database (as main schema) that will work later when I connect to the database via ATTACH?
From what it looks like, a trigger AFTER INSERT will fire after every single line of insert. If it inserts stuff into another table, does that mean I'm increasing my number of transactions from 2 to 1+N? Or is there a mechanism that speeds up triggered interaction? The first case would slow down things horribly.
Is there any chance that a FULL OUTER JOIN (I know that I need to create it from other JOIN commands) is faster than filling a registry with insertion transactions every time? We're talking roughly ten transactions per second with an average of 1000 elements (insert) vs. one query of 30000 every two seconds (query).
Open the sqlite3 databases in multi-threading mode, handle the insert/update/query/delete functions by separate threads. I prefer to transfer query result to a stl container for processing.
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.
Let's say I have a (MySQL) DB. I want to automate the update of this database via an application, that will:
1. Import from DB
2. Calculate updated data
3. Export back updated data
The timing is important, I don't want to import while calculating, in fact I don't want any queries then; I want to import (a) table(s) as a whole, then calculate. So, my question is, if a row is represented with an instance of a class, then what container do I put these objects into?
A vector? A set? What about ordered vs. unordered? Just use what seems best for my case according to big O times? Any special traps to fall into here? Is this case no different than with data "born in memory", so the only things to consider besides size overhead are "do I want the lookup or the insertion to be faster" ?
Probably the best route is to use some ORM, but let's say I don't want to.
I've seen some apps use boost::unordered_set, and I wondered, if there is a particular reason for its use...
I use a jdbc-like interface as the connector (libmysqlcpp).
I do not think that the container you have to use can be guessed with so few information. It mainly depends of the data size, type and the algorithm you will run.
But my main concern over such a design is that it will quickly choke your network or your base and database. If you have a big table you'll:
select all the data from the table
retrieve all the data over the network
process on you machine part (some columns ?) or the entirety of the data
push the data over the network
update your rows (or erase/replace maybe)
Why don't you consider working directly on the mysql server ? You create your user defined function that work on the directly data, saving the network and even taking advantage of the fact that mysql is built to handle gigantic amount of data, quantity that an in-memory container is not built to handle.
I am currently developing an application for Azure Table Storage. In that application I have table which will have relatively few inserts (a couple of thousand/day) and the primary key of these entities will be used in another table, which will have billions of rows.
Therefore I am looking for a way to use an auto-incremented integer, instead of GUID, as primary key in the small table (since it will save lots of storage and scalability of the inserts is not really an issue).
There've been some discussions on the topic, e.g. on http://social.msdn.microsoft.com/Forums/en/windowsazure/thread/6b7d1ece-301b-44f1-85ab-eeb274349797.
However, since concurrency problems can be really hard to debug and spot, I am a bit uncomfortable with implementing this on own. My question is therefore if there is a well tested impelemntation of this?
For everyone who will find it in search, there is a better solution. Minimal time for table lock is 15 seconds - that's awful. Do not use it if you want to create a truly scalable solution. Use Etag!
Create one entity in table for ID (you can even name it as ID or whatever).
1) Read it.
2) Increment.
3) InsertOrUpdate WITH ETag specified (from the read query).
if last operation (InsertOrUpdate) succeeds, then you have a new, unique, auto-incremented ID. If it fails (exception with HttpStatusCode == 412), it means that some other client changed it. So, repeat again 1,2 and 3.
The usual time for Read+InsertOrUpdate is less than 200ms. My test utility with source on github.
See UniqueIdGenerator class by Josh Twist.
I haven't implemented this yet but am working on it ...
You could seed a queue with your next ids to use, then just pick them off the queue when you need them.
You need to keep a table to contain the value of the biggest number added to the queue. If you know you won't be using a ton of the integers, you could have a worker every so often wake up and make sure the queue still has integers in it. You could also have a used int queue the worker could check to keep an eye on usage.
You could also hook that worker up so if the queue was empty when your code needed an id (by chance) it could interupt the worker's nap to create more keys asap.
If that call failed you would need a way to (tell the worker you are going to do the work for them (lock), then do the workers work of getting the next id and unlock)
lock
get the last key created from the table
increment and save
unlock
then use the new value.
The solution I found that prevents duplicate ids and lets you autoincrement it is to
lock (lease) a blob and let that act as a logical gate.
Then read the value.
Write the incremented value
Release the lease
Use the value in your app/table
Then if your worker role were to crash during that process, then you would only have a missing ID in your store. IMHO that is better than duplicates.
Here is a code sample and more information on this approach from Steve Marx
If you really need to avoid guids, have you considered using something based on date/time and then leveraging partition keys to minimize the concurrency risk.
Your partition key could be by user, year, month, day, hour, etc and the row key could be the rest of the datetime at a small enough timespan to control concurrency.
Of course you have to ask yourself, at the price of date in Azure, if avoiding a Guid is really worth all of this extra effort (assuming a Guid will just work).