I configured a CloudWatch alarm. I need this event to be captured and logged in a database for reporting purpose. Dynamo is chosen because of the low volume of incoming read/write.
What I need to capture:
AWS account ID (12345678912345)
Datetime of the event
Event ID
I have multiple AWS accounts, and I want to store them all in a single table (I could store them in separate table as well, but given the low volume not sure if that's really that useful).
So should I use Hash+Range?
Hash: <account_id>
Range: <datetime>
This way my understanding is DynamoDB will group/order based on the range.
My queries would only be:
get all of the events for account_id / for all accounts
get all of the events since x time for account_id / for all accounts
Is this a good design? Do I need a separate index?
As per your query patterns, you approach looks correct.
If you want data for only one account_id, do a Query. You can also supply a KeyConditionExpression on range-key to only get events that happened after given timestamp.
If you want data for list of account_ids, run a Scan. (You can't do a BatchGetItem, because it needs both hash-key and range-key).
Related
How is aggregation achieved with dynamodb? Mongodb and couchbase have map reduce support.
Lets say we are building a tech blog where users can post articles. And say articles can be tagged.
user
{
id : 1235,
name : "John",
...
}
article
{
id : 789,
title: "dynamodb use cases",
author : 12345 //userid
tags : ["dynamodb","aws","nosql","document database"]
}
In the user interface we want to show for the current user tags and the respective count.
How to achieve the following aggregation?
{
userid : 12,
tag_stats:{
"dynamodb" : 3,
"nosql" : 8
}
}
We will provide this data through a rest api and it will be frequently called. Like this information is shown in the app main page.
I can think of extracting all documents and doing aggregation at the application level. But I feel my read capacity units will be exhausted
Can use tools like EMR, redshift, bigquery, aws lambda. But I think these are for datawarehousing purpose.
I would like to know other and better ways of achieving the same.
How are people achieving dynamic simple queries like these having chosen dynamodb as primary data store considering cost and response time.
Long story short: Dynamo does not support this. It's not build for this use-case. It's intended for quick data access with low-latency. It simply does not support any aggregating functionality.
You have three main options:
Export DynamoDB data to Redshift or EMR Hive. Then you can execute SQL queries on a stale data. The benefit of this approach is that it consumes RCUs just once, but you will stick with outdated data.
Use DynamoDB connector for Hive and directly query DynamoDB. Again you can write arbitrary SQL queries, but in this case it will access data in DynamoDB directly. The downside is that it will consume read capacity on every query you do.
Maintain aggregated data in a separate table using DynamoDB streams. For example you can have a table UserId as a partition key and a nested map with tags and counts as an attribute. On every update in your original data DynamoDB streams will execute a Lambda function or some code on your hosts to update aggregate table. This is the most cost efficient method, but you will need to implement additional code for each new query.
Of course you can extract data at the application level and aggregate it there, but I would not recommend to do it. Unless you have a small table you will need to think about throttling, using just part of provisioned capacity (you want to consume, say, 20% of your RCUs for aggregation and not 100%), and how to distribute your work among multiple workers.
Both Redshift and Hive already know how to do this. Redshift relies on multiple worker nodes when it executes a query, while Hive is based on top of Map-Reduce. Also, both Redshift and Hive can use predefined percentage of your RCUs throughput.
Dynamodb is pure key/value storage and does not support aggregation out of the box.
If you really want to do aggregation using DynamoDB here some hints.
For you particular case lets have table named articles.
To do aggregation we need an extra table user-stats holding userId and tag_starts.
Enabled DynamoDB streams on table articles
Create a new lambda function user-stats-aggregate which is subscribed to articles DynamoDB stream and received OLD_NEW_IMAGES on every create/update/delete operation over articles table.
Lambda will perform following logic
If there is no old image, get current tags and increase by 1 every occurrence in the db for this user. (Keep in mind there could be the case there is no initial record in user-stats this user)
If there is old image see if tag was added or removed and apply change +1 or -1 depending on the case for each affected tag for received user.
Stand an API service retrieving these user stats.
Usually aggregation in DynamoDB could be done using DynamoDB streams , lambdas for doing aggregation and extra tables keeping aggregated results with different granularity.(minutes, hours, days, years ...)
This brings near realtime aggregation without need to do it on the fly per every request, you query on aggregated data.
Basic aggregation can be done using scan() and query() in lambda.
I am currently having a problem with solving the problem with dynamo db querying.
My dynamo db keeps track of changes in data. Thus partition key identifies which data i am changing
a single row looks something like this
partition key: servicename#resource#resource_id#region
sortkey: current_time
changelogs: map of changelog (basically an array of changelog)
changer: who changed it
; It does great job when requesting one specific resource changes; however if i want to, say, query "I want to see last 30 minutes of changes in this servicename#resource without specifying resource id. Right now I only have scanning method at hand.. And i can't use scan due to large amount of data. I am open to all recommendation.
Your subject mentions the answer...even if your post doesn't.
Create a Global secondary index (GSI) with either the date or part of the date as the hash key and the time or remaining date part + time as the sort key. If you have a large amount of data being created, you might want to include Hour in the hash key.
HK SK
YYYY-MM-DD-HH :MM:SS.00000
YYYY-MM-DD HH:MM:SS.00000
YYYY-MM DD-HH:MM:SS.0000
I am currently developing a location based service on GAE/Java. I am quite new to this and I need your help with the JDO query part.
I have two persistent classes, Client and ClientGeolocation. The first one is for storing the client attributes (Key clientId, String name, String settings, etc.) and the second is for storing its geolocation updates (Key clientGeolocationId, Key clientId, Long timestamp, Double latitude, Double longitude). Since one client has thousands of geolocation records (one for each location update) over time, I decided to use 1-to-1 unowned relationship between ClientGeolocation and Client classes.
The service lets the user to see if another user is within range (e.g. they are in 5 minutes walking distance). Making this happen with JDO queries for each request would be far too resource consuming / slow so I put the last geolocation of the users in memcache and do the checking from there. So far so good.
The problem is when the App cold starts and memcache is empty, I want to fill up the memcache with data from the storage (using JDO query) and I simply do not know how to query "the last geolocation record for each user who has at least one record which is not older than 180 minutes".
The best possible solution I can come up with at the moment is to do this in two parts. First, to query the clientId keys of users who has records within the last 180 minutes (this will query distinct clientIds I hope) then execute a query for all clientId in which I query the last (top 1 order by timestamp desc) geolocation record. This means if the first query returns 10.000 users then I will do 10.000 queries for the last geolocation records. I have a feeling that there is a better solution for this in GAE :) .
Can you please help me how to write this query in a proper way?
Thank you very much for your help!
Should this be helpful ?
http://www.datanucleus.org/products/accessplatform_3_0/jdo/jdoql_subquery.html
I've got a problem with performance of my reporting database (tables have millions of records, 50+), when I want to calculate distinct on column that indicates a visitor uniqueness, let's say some hashkey.
For example:
I have these columns:
hashkey, name, surname, visit_datetime, site, gender, etc...
I need to get distinct in time span of 1 year, less than in 5 sec:
SELECT COUNT(DISTINCT hashkey) FROM table WHERE visit_datetime BETWEEN 'YYYY-MM-DD' AND 'YYYY-MM-DD'
This query will be fast for short time ranges, but if it be bigger than one month, than it can takes more than 30s.
Is there a better technology to calculate something like this than relational databases?
I'm wondering what google analytics use to do theirs unique visitors calculating on the fly.
For reporting and analytics, the type of thing you're describing, these sorts of statistics tend to be pulled out, aggregated, and stored in a data warehouse or something. They are stored in a fashion meant for performance reasons in lieu of nice relational storage techniques optimized for OLTP (online transaction processing). This pre-aggregated technique is called OLAP (online analytical processing).
You could have another table store the count of unique visitors for each day, updated daily by a cron function or something.
Google Analytics uses a first-party cookie, which you can see if you log Request Headers using LiveHTTPHeaders, etc.
All GA analytics parameters are packed into the Request URL, e.g.,
utma%3D97315849.1774621898.1207701397.1207701397.1207701397.1%3B">http://www.google-analytics.com/_utm.gif?utmwv=4&utmn=769876874&utmhn=example.com&utmcs=ISO-8859-1&utmsr=1280x1024&utmsc=32-bit&utmul=en-us&utmje=1&utmfl=9.0%20%20r115&utmcn=1&utmdt=GATC012%20setting%20variables&utmhid=2059107202&utmr=0&utmp=/auto/GATC012.html?utm_source=www.gatc012.org&utm_campaign=campaign+gatc012&utm_term=keywords+gatc012&utm_content=content+gatc012&utm_medium=medium+gatc012&utmac=UA-30138-1&utmcc=__utma%3D97315849.1774621898.1207701397.1207701397.1207701397.1%3B...
Within that URL is a piece that keyed to __utmcc, these are the GA cookies. Within _utmcc, is a string keyed to _utma, which is string comprised of six fields each delimited by a '.'. The second field is the Visitor ID, a random number generated and set by the GA server after looking for GA cookies and not finding them:
__utma%3D97315849.1774621898.1207701397.1207701397.1207701397.1
In this example, 1774621898 is the Visitor ID, intended by Google Analytics as a unique identifier of each visitor
So you can see the flaws of technique to identify unique visitors--entering the Site using a different browser, or a different device, or after deleting the cookies, will cause you to appear to GA as a unique visitor (i.e., it looks for its cookies and doesn't find any, so it sets them).
There is an excellent article by EFF on this topic--i.e., how uniqueness can be established, and with what degree of certainty, and how it can be defeated.
Finally, once technique i have used to determine whether someone has visited our Site before (assuming the hard case, which is that they have deleted their cookies, etc.) is to examine the client request for our favicon. The directories that store favicons are quite often overlooked--whether during a manual sweep or programmatically using a script.
I need to do transactions (begin, commit or rollback), locks (select for update).
How can I do it in a document model db?
Edit:
The case is this:
I want to run an auctions site.
And I think how to direct purchase as well.
In a direct purchase I have to decrement the quantity field in the item record, but only if the quantity is greater than zero. That is why I need locks and transactions.
I don't know how to address that without locks and/or transactions.
Can I solve this with CouchDB?
No. CouchDB uses an "optimistic concurrency" model. In the simplest terms, this just means that you send a document version along with your update, and CouchDB rejects the change if the current document version doesn't match what you've sent.
It's deceptively simple, really. You can reframe many normal transaction based scenarios for CouchDB. You do need to sort of throw out your RDBMS domain knowledge when learning CouchDB, though. It's helpful to approach problems from a higher level, rather than attempting to mold Couch to a SQL based world.
Keeping track of inventory
The problem you outlined is primarily an inventory issue. If you have a document describing an item, and it includes a field for "quantity available", you can handle concurrency issues like this:
Retrieve the document, take note of the _rev property that CouchDB sends along
Decrement the quantity field, if it's greater than zero
Send the updated document back, using the _rev property
If the _rev matches the currently stored number, be done!
If there's a conflict (when _rev doesn't match), retrieve the newest document version
In this instance, there are two possible failure scenarios to think about. If the most recent document version has a quantity of 0, you handle it just like you would in a RDBMS and alert the user that they can't actually buy what they wanted to purchase. If the most recent document version has a quantity greater than 0, you simply repeat the operation with the updated data, and start back at the beginning. This forces you to do a bit more work than an RDBMS would, and could get a little annoying if there are frequent, conflicting updates.
Now, the answer I just gave presupposes that you're going to do things in CouchDB in much the same way that you would in an RDBMS. I might approach this problem a bit differently:
I'd start with a "master product" document that includes all the descriptor data (name, picture, description, price, etc). Then I'd add an "inventory ticket" document for each specific instance, with fields for product_key and claimed_by. If you're selling a model of hammer, and have 20 of them to sell, you might have documents with keys like hammer-1, hammer-2, etc, to represent each available hammer.
Then, I'd create a view that gives me a list of available hammers, with a reduce function that lets me see a "total". These are completely off the cuff, but should give you an idea of what a working view would look like.
Map
function(doc)
{
if (doc.type == 'inventory_ticket' && doc.claimed_by == null ) {
emit(doc.product_key, { 'inventory_ticket' :doc.id, '_rev' : doc._rev });
}
}
This gives me a list of available "tickets", by product key. I could grab a group of these when someone wants to buy a hammer, then iterate through sending updates (using the id and _rev) until I successfully claim one (previously claimed tickets will result in an update error).
Reduce
function (keys, values, combine) {
return values.length;
}
This reduce function simply returns the total number of unclaimed inventory_ticket items, so you can tell how many "hammers" are available for purchase.
Caveats
This solution represents roughly 3.5 minutes of total thinking for the particular problem you've presented. There may be better ways of doing this! That said, it does substantially reduce conflicting updates, and cuts down on the need to respond to a conflict with a new update. Under this model, you won't have multiple users attempting to change data in primary product entry. At the very worst, you'll have multiple users attempting to claim a single ticket, and if you've grabbed several of those from your view, you simply move on to the next ticket and try again.
Reference: https://wiki.apache.org/couchdb/Frequently_asked_questions#How_do_I_use_transactions_with_CouchDB.3F
Expanding on MrKurt's answer. For lots of scenarios you don't need to have stock tickets redeemed in order. Instead of selecting the first ticket, you can select randomly from the remaining tickets. Given a large number tickets and a large number of concurrent requests, you will get much reduced contention on those tickets, versus everyone trying to get the first ticket.
A design pattern for restfull transactions is to create a "tension" in the system. For the popular example use case of a bank account transaction you must ensure to update the total for both involved accounts:
Create a transaction document "transfer USD 10 from account 11223 to account 88733". This creates the tension in the system.
To resolve any tension scan for all transaction documents and
If the source account is not updated yet update the source account (-10 USD)
If the source account was updated but the transaction document does not show this then update the transaction document (e.g. set flag "sourcedone" in the document)
If the target account is not updated yet update the target account (+10 USD)
If the target account was updated but the transaction document does not show this then update the transaction document
If both accounts have been updated you can delete the transaction document or keep it for auditing.
The scanning for tension should be done in a backend process for all "tension documents" to keep the times of tension in the system short. In the above example there will be a short time anticipated inconsistence when the first account has been updated but the second is not updated yet. This must be taken into account the same way you'll deal with eventual consistency if your Couchdb is distributed.
Another possible implementation avoids the need for transactions completely: just store the tension documents and evaluate the state of your system by evaluating every involved tension document. In the example above this would mean that the total for a account is only determined as the sum values in the transaction documents where this account is involved. In Couchdb you can model this very nicely as a map/reduce view.
No, CouchDB is not generally suitable for transactional applications because it doesn't support atomic operations in a clustered/replicated environment.
CouchDB sacrificed transactional capability in favor of scalability. In order to have atomic operations you need a central coordination system, which limits your scalability.
If you can guarantee you only have one CouchDB instance or that everyone modifying a particular document connects to the same CouchDB instance then you could use the conflict detection system to create a sort of atomicity using methods described above but if you later scale up to a cluster or use a hosted service like Cloudant it will break down and you'll have to redo that part of the system.
So, my suggestion would be to use something other than CouchDB for your account balances, it will be much easier that way.
As a response to the OP's problem, Couch is probably not the best choice here. Using views is a great way to keep track of inventory, but clamping to 0 is more or less impossible. The problem being the race condition when you read the result of a view, decide you're ok to use a "hammer-1" item, and then write a doc to use it. The problem is that there's no atomic way to only write the doc to use the hammer if the result of the view is that there are > 0 hammer-1's. If 100 users all query the view at the same time and see 1 hammer-1, they can all write a doc to use a hammer 1, resulting in -99 hammer-1's. In practice, the race condition will be fairly small - really small if your DB is running localhost. But once you scale, and have an off site DB server or cluster, the problem will get much more noticeable. Regardless, it's unacceptable to have a race condition of that sort in a critical - money related system.
An update to MrKurt's response (it may just be dated, or he may have been unaware of some CouchDB features)
A view is a good way to handle things like balances / inventories in CouchDB.
You don't need to emit the docid and rev in a view. You get both of those for free when you retrieve view results. Emitting them - especially in a verbose format like a dictionary - will just grow your view unnecessarily large.
A simple view for tracking inventory balances should look more like this (also off the top of my head)
function( doc )
{
if( doc.InventoryChange != undefined ) {
for( product_key in doc.InventoryChange ) {
emit( product_key, 1 );
}
}
}
And the reduce function is even more simple
_sum
This uses a built in reduce function that just sums the values of all rows with matching keys.
In this view, any doc can have a member "InventoryChange" that maps product_key's to a change in the total inventory of them. ie.
{
"_id": "abc123",
"InventoryChange": {
"hammer_1234": 10,
"saw_4321": 25
}
}
Would add 10 hammer_1234's and 25 saw_4321's.
{
"_id": "def456",
"InventoryChange": {
"hammer_1234": -5
}
}
Would burn 5 hammers from the inventory.
With this model, you're never updating any data, only appending. This means there's no opportunity for update conflicts. All the transactional issues of updating data go away :)
Another nice thing about this model is that ANY document in the DB can both add and subtract items from the inventory. These documents can have all kinds of other data in them. You might have a "Shipment" document with a bunch of data about the date and time received, warehouse, receiving employee etc. and as long as that doc defines an InventoryChange, it'll update the inventory. As could a "Sale" doc, and a "DamagedItem" doc etc. Looking at each document, they read very clearly. And the view handles all the hard work.
Actually, you can in a way. Have a look at the HTTP Document API and scroll down to the heading "Modify Multiple Documents With a Single Request".
Basically you can create/update/delete a bunch of documents in a single post request to URI /{dbname}/_bulk_docs and they will either all succeed or all fail. The document does caution that this behaviour may change in the future, though.
EDIT: As predicted, from version 0.9 the bulk docs no longer works this way.
Just use SQlite kind of lightweight solution for transactions, and when the transaction is completed successfully replicate it, and mark it replicated in SQLite
SQLite table
txn_id , txn_attribute1, txn_attribute2,......,txn_status
dhwdhwu$sg1 x y added/replicated
You can also delete the transactions which are replicated successfully.