For an ETL job which needs to compute and load over MySQL table rows (At max 30,000 rows with at max 15 small sized columns), which is a better candidate (AWS batch vs Lambda)?
Also want to understand if there any other better options to consider. Would be helpful if one could provide the parameters over which this decision should be made
Batch jobs are used particularly in background work loads while Lambda is useful for near/real time work loads.
Also do consider the business logic complexity while making decision
For more info:
Why/When use batch job: Batch FAQs
Why/When use lambda: Lambda FAQs
Related
I have thousands of training jobs that I want to run on sagemaker. Basically I have a list of hyperparameters and I want to train the model for all of those hyperparmeters in parallel (not a standard hyperparameter tuning where we just want to optimize the hyperparameter, here we want to train for all of the hyperparameters). I have searched the docs quite extensively but it surprises me that I couldn't find any info about this, even though it seems like a pretty basic functionality.
For example, let's say I have 10,000 training jobs, and my quota is 20 instances, what is the best way to run these jobs utilizing all my available instances? In particular,
Is there a "queue manager" functionality that takes the list of hyperparameters and runs the training jobs in batches of 20 until they are all done (even better if it could keep track of failed/completed jobs).
Is it best practice to run a single training job per instance? If that's the case do I need to ask for a much higher quota on the number of instance?
If this functionality does not exist in sagemaker, is it worth using EC2 instead since it's a bit cheaper?
Your question is very broad and the best way forward would depend on other details of your use-case, so we will have to make some assumptions.
[Queue manager]
SageMaker does not have a queue manager. If at the end you decide you need a queue manager, I would suggest looking towards AWS Batch.
[Single vs multiple training jobs]
Since you need to run 10s of thousands job I assume you are training fairly lightweight models, so to save on time, you would be better off reusing instances for multiple training jobs. (Otherwise, with 20 instances limit, you need 500 rounds of training, with a 3 min start time - depending on instance type - you need 25 hours just for the wait time. Depending on the complexity of each individual model, this 25hours might be significant or totally acceptable).
[Instance limit increase]
You can always ask for a limit increase, but going from a limit of 20 to 10k at once is likely that will not be accepted by the AWS support team, unless you are part of an organisation with a track record of usage on AWS, in which case this might be fine.
[One possible option] (Assuming multiple lightweight models)
You could create a single training job, with instance count, the number of instances available to you.
Inside the training job, your code can run a for loop and perform all the individual training jobs you need.
In this case, you will need to know which which instance is which so you can make the split of the HPOs. SageMaker writes this information on the file: /opt/ml/input/config/resourceconfig.json so using that you can easily have each instance run a subset of the trainings required.
Another thing to think of, is if you need to save the generated models (which you probably need). You can either save everything in the output model directory - standard SM approach- but this would zip all models in a model.tar.gz file.
If you don't want this, and prefer to have each model individually saved, I'd suggest using the checkpoints directory that will sync anything written there to your s3 location.
I have a set of calculations that needs to run in a batch, and the workload is easily parallelized across machines. The work to be done is already done within a Docker container. I'm trying to understand the easiest way for me to run this workload in a highly parallel way on AWS. However, in trying to figure out where to begin I'm having trouble finding the right entrypoint. I read about AWS Batch and AWS Fargate, but each time I try to go down one of those paths to learn about them in more detail, more AWS services start popping up (Lamdas, Step Functions, ECS, AutoScaling groups), with each article having a different combination. Furthermore, I start thinking about the problem as a Batch vs Fargate problem, and then I find another article that talks about Batch + Fargate, or X + ECS + ....
I'm having trouble finding the appropriate introduction to the choices so I can get started with setting something up and getting some experience. Any pointers on which direction I might go or some resources for me to look at?
AWS containers services team member here. Your question triggers all my button cause I have been working on a deliverable to address some of this confusion ("where do I start with xyz?"). I can try to answer your question briefly here but if you want to read more (perhaps way more than you'd need feel free to contact me offline (mreferre at amazon dot com will work).
First and foremost it's not a Vs but it's an AND. Think of all these products you mention being distributed at different layers of the stack (this is a draft visual in the deliverable):
Fargate represents capacity (where your container is running), ECS represents a core containers orchestrator and Batch is one of the provisioners on top of the container orchestrator. Lambda is something separate and that live on its own. The options for your specific use case seem to be:
Lambda
ECS/Fargate
Batch/ECS/Fargate
Step Functions/ECS/Fargate (this one is outside of analysis and you don't see it in my visual - wondering if I should add it).
As others have hinted you probably want to use Lambda if your model is event-driven (e.g. if you want to fire up a dedicated function for every event like a new file uploaded to S3).
You probably do not want to use a naked ECS/Fargate solution because it would require more work to deal with the triggering and the scheduling of your batch jobs.
You probably want to use either Batch or Step Functions to schedule jobs on ECS/Fargate. I'd argue SF is good if you have basic workflows that you need to deal with and Batch if you need to manage complex jobs at scale. Perhaps this 35 mins presentation that I did last year can provide a bit more background on these Batch Vs SF differences.
Let me know if you have any additional questions because this discussion is super useful for the positioning I am trying to build.
At a high / theoretical level I know exactly the type of architecture I want to build and how it would work, but I'm attempting to construct this as cheaply as possible using AWS services and my lack of familiarity with the offerings of AWS has me running in circles.
The Data
We run a video streaming platform. On busy nights we have about 100 simultaneous live streams going with upwards of 30,000 viewers. We expect this number to rise to 100,000 in the next few years. A live stream lasts, on average, 2 hours.
We send a heartbeat from our player every 10 seconds with information about the viewer -- how much data they've viewed, how much data they've buffered, what quality they're streaming, etc.
These heartbeats are sent directly to an AWS Kinesis endpoint.
Finally, we want to retain all past messages for at least 5 years (hopefully longer) so that we can look at historic analytics.
Some back of the envelope calculations suggest we will have 0.1 * 60 * 60 * 2 * 100000 * 365 * 5 = 131 billion heartbeat messages five years from now.
Our Old Pipeline
Our old system had a single Kinesis consumer. Aggregate data was stored in DynamoDB. Whenever a message arrived we would read the record from DynamoDB, update the record, then write the new record back. This read-update-write loop limited the speed at which we could process messages and made it so that each message coming in was dependent on the messages before it, so they could not be processed in parallel.
Part of the reason for this setup is that our message schema was not well designed from the outset. We send the timestamp at which the message was sent, but we do not send "amount of video watched since last heartbeat". As a result in order to compute the total viewer time we need to look up the last heartbeat message sent by this player, subtract the timestamps, and add that value. Similar issues exist with many other metrics.
Our New Pipeline
We've begun to run into scaling issues. During our peak hours analytics can be delayed by as much as four hours while waiting for a backlog of messages to be processed. If this backlog reaches 24 hours Kinesis will start deleting data. So we need to fix our pipeline to remove this dependency on past messages so we can process them in parallel.
The first part of this was updating the messages sent by our players. Our new specification includes only metrics that can be trivially sum'd with no subtraction. So we can just keep adding to the "time viewed" metric, for instance, without any regard to past messages.
The second part of this was ensuring that Kinesis never backs up. We dump the raw messages to S3 as quickly as they arrive with no processing (Kinesis Data Fire Hose) so that we can crunch analytics on them at our leisure.
Finally, we now want to actually extract information from these analytics as quickly as possible. This is where I've hit a snag.
The Questions We Want to Answer
As this is an analytics pipeline, our questions mostly revolve around filtering these messages and then aggregating fields for the remaining messages (possibly, in fact likely, with grouping). For instance:
How many Android users watched last night's stream in HD? (FILTER by stream and OS)
What's the average bandwidth usage among all users? (SUM and COUNT, with later division of the final aggregates which could be done on the dashboard side)
What percent of users last year were on any Apple device (iOS, tvOS, etc)? (COUNT, grouped by OS)
What's the average time spent buffering among Android users for streams in the past year? (a mix of all of the above)
Options
AWS Athena would allow us to query the data in S3 directly as if it were an ANSI SQL table. However reading up on Athena, unless the data is properly formatted it can be incredibly slow. Some benchmarks I've seen show that processing 1.1 billion rows of CSV data can take up to 2 minutes. I'm looking at processing 100x that much data
AWS EMR and AWS Redshift sound like they are built for this purpose, but are complicated to set up and have a high base cost to run (requiring an EC2 cluster to remain active at all times). AWS Redshift also requires data be loaded into it, which sounds like it might be a very slow process, delaying our access to analytics
AWS Glue sounds like it may be able to take the raw messages as they arrive in S3 and convert them to Parquet files for more rapid querying via Athena
We could run a job to regularly batch messages to reduce the total number that must be processed. While a stream is live we'll receive one message every 10 seconds, but we really only care about the totals for a given viewer. This means that when a 2-hour stream concludes we can combine the 720 messages we've received from that player into a single "summary" message about the viewer's experience during the whole stream. This would massively reduce the amount of data we need to process, but exactly how and when to trigger this process isn't clear to me
The Ideal Architecture
This is a Big Data problem. The generic solution to Big Data problems is "don't take your data to your query, take your query to your data". If these messages were spread across 100 small storage nodes then each node could filter, sum, and count the subset of data they hold and pass these aggregates back to a central node which sums the sums and sums the counts. If each node is only operating on 1/100th of the data set then this kind of processing could theoretically be incredibly fast.
My Confusion
While I have a theoretical understanding of the "ideal" architecture, it's not clear to me if AWS works this way or how to construct a system that will function well like this.
S3 is a black box. It's not clear if Athena queries are run on individual nodes and aggregates are further reduced elsewhere, or if there's a system reading all of the data and aggregating it in a central location
Redshift requires the data by copied into a Redshift database. This doesn't sound fast, nor distributed
It's unclear to me how EMR works or if it will suit my purpose. Still researching
AWS Glue seems like it may need to be triggered by some event?
Parquet files seems to be like CSVs, where multiple records reside in a single file. Meanwhile I'm dumping one record per file. But perhaps there's a way to fix that? e.g. batching files every minute or every 5 minutes?
RDS or a similar service might be really good for this (indexing and whatnot) but would require a guaranteed schema (or necessitate migrating if our message schema changed) which is a concern. Migrating terabytes of data if we change our message schema sounds out of the question
Finally, along with wanting to get analytics results in as "real time" as possible (ideally we want to know within 1 minute when someone joins or leaves a stream), we want the dashboards to load quickly. Waiting 30 seconds to see the count of live viewers is horrendous. Dashboards should load in 2 seconds or less (ideally)
The plan is to use QuickSight to create dashboards (our old system had a hack-y Django app that read from our DynamoDB aggregates table, but I'd like to avoid creating more code for people to maintain)
I expect you are going to get a lot of different answers and opinions from the broad set of experts you have pinged with this. There is likely no single best answer to this as there are a lot of variables. Let me give you my best advice based on my experience in the field.
Kinesis to S3 is a good start and not moving data more than needed is the right philosophy.
You didn't mention Kinesis Data Analytics and this could be a solution for SOME of your needs. It is best for questions about what is happening in the data feed right now. The longer timeframe questions are better suited for the tools you mention. If you aren't too interested in what is happening in the past 10 minutes (or so) it could be good to omit.
S3 organization will be key to performing any analytic directly on the data there. You mention parquet formatting which is good but partitioning is far more powerful. Organizing the S3 data into "days" or "hours" of data and setting up the partitioning based on this can greatly speed up any query that is limited in the amount of time that is needed (don't read what you don't need).
Important safety note on S3 - S3 is an object store and as such there is overhead for each object you reference. Having many small objects (10,000+) treated as a single set of data is going to be slow no matter what solution you go with. You need to fix this before you go forward with any solution. You see it takes upwards of .5 sec to look up an object in S3 but if the file is small the transfer time is next to nothing. Now multiply .5 sec times all the objects you have and see how long it will take to read them. This is not a function of the downstream tool you choose but of the S3 organization you have. S3 objects as part of a Big Data solution should be at least 100M in size to not suffer greatly from the object lookup time. The choice of parquet or CSV files is mute without addressing object size and partitioning first.
Athena is good for occasional queries especially if the date ranges are limited. Is this the query pattern you expect? As you say "move the compute to the data" but if you use Athena to do large cross-sectional analytics where a large percentage of the data needs to be used, you are just moving the data to Athena every time you execute this query. Don't stop thinking about data movement at the point it is stored - think about the data movements to do the analytics also.
So a big question is how much data is needed and how often to support your analytics workloads and BI functions? This is the end result you are looking for. If a high percentage of the data is needed frequently then a warehouse solution like Redshift with the data loaded to disk is the right answer. The data load time to Redshift is quite fast as it parallel loads the data from S3 (you see S3 is a cluster and Redshift is a cluster and parallel loads can be done). If loading all your data into Redshift is what you need then the load time is not your main concern - the cost is. Big powerful tool with a price tag to match. The new RA3 instance type bends this curve down significantly for large data size clusters so could be a possibility.
Another tool you haven't mentioned is Redshift Spectrum. This brings several powerful technologies together that could be important to you. First is the power of Redshift with the ability to choose smaller cluster sizes that normally would be used for your data size. S3 filtering and aggregation technology allows Spectrum to perform actions on the data in S3 (yes initial compute actions of the query are performed inside of S3 potentially greatly reducing the data moved to Redshift). If your query patterns support this data reduction in S3 then the data movement will be small and the Redshift cluster can be small (cheap) too. This can be a powerful compromise point for IoT solutions like yours since complex data models and joining are not needed.
You bring up Glue and conversion to parquet. These can be good to do but as I mentioned before partitioning of the data in S3 is usually far more powerful. The value of parquet will increase as the width of your data increases. Parquet is a columnar format so it is advantaged if only a subset of "columns" are needed. The downside is the conversion time/cost and the loss of easy human readability (which can be huge during debug).
EMR is another choice you mention but I generally advise clients against going with EMR unless they need the flexibility it brings to the analytics and they have the skills to use it well. Without these EMR tends to be an unneeded costs sink.
If this is really going to be a Big Data solution then RDS (and Aurora) not good choices. They are designed for transactional workloads, not analytics. The data size and analytics will not fit well or be cost effective.
Another tool in the space is S3 Select. Not likely what you are looking for but something to remember exists and can be a tool in the toolbox.
Hybrid solutions are common in this space if there are variable needs based on some factor. A common one "is time of day" - no one is running extensive reports at 3am so the needed performance is much less. Another is user group - some groups need simple analytics while others need much more power. Another factor is timeliness of data - does everyone need "up to the second" information or is daily information sufficient? Trying to have one tool that does everything for everybody, all the time is often a path to an expensive, oversized solution.
Since Redshift Spectrum and Athena can point at the same S3 data (well organized since both will benefit) both tools can coexist on the same data. Also, Redshift is ideal for sifting through huge mounds of data, it is ideal for producing summary tables and then writing them (in partitioned parquet) to S3 for tools like Athena to use. All these cloud services can be run on schedules and this includes Redshift and EMR (Athena is query on demand) so they don't need to run all the time. Redshift with Spectrum can run a few hours a day to perform deep analytics and summarize data for writing to S3. Your data scientist can also use Redshift for their hardcore work while Athena supports dashboards using the daily summary data and Kinesis Data Analytics as source.
Lastly you bring up a 2 sec requirement for dashboards. This is definitely possible with Quicksight backed up by Redshift or Athena but won't be met for arbitrarily complex / data intensive queries. To meet this you will need the engine to have enough horsepower to produce the data in question. Redshift with local data storage is likely the fastest (Redshift Spectrum with some data pruning done in S3 wins in some cases) and Athena is the weakest / slowest. But the power doesn't matter if the work is small - see your query workload will be a huge deciding factor. The fastest will be to load the needed data into Quicksight storage (SPICE) but this is another localized / summarized version of the data so timeliness is again a factor (how often is this updated).
Based on designing similar systems and a bunch of guesses as to what you need I'd recommend that you:
Fix your object size (Kineses can be configured to do this)
Partition your data by day
Set up a small Redshift cluster (4 X dc2.large) and use Spectrum source address the data
Connect Quicksight to Redshift
Measure the performance (and cost) and compare to requirements (there will likely be gaps)
Adjust to solution (summary tables to S3, Athena, SPICE etc.) to meet goals
The alternative is to hire someone who has set up such systems before and have them review the requirements in detail and make a less "guess-based" recommendation.
I would look into Druid. Not an AWS offering, but easily runs on AWS, with good integration with S3 and Kinesis.
Capable of reading from Kinesis, at high speeds, and make the data available for querying right away. Can also flatten and transform the data as it reads it.
Capable of doing rollups/aggregation/compaction during ingestion (and further reduce data in an async manner). From what you wrote, it seems to me that it could easily reduce the number of rows in the DB by a very large factor.
Capable of fast queries, using standard SQL.
Smart partitioning of the data to scan only the relevant dates.
The down-side is that you will need to keep a cluster up and running for ingestion and for querying. It is pretty scalable, so you can start small.
On the up-side - you're not using 10 different technologies (Athena/Glue/EMR/etc.)
You might want to consider contacting Imply, which can ease the deployment.
A usual approach a lot of companies take is they do heavy weight lifting in athena or bigquery (or some other distributed sql environment) -> aggregate intermediate results into multiple indexed+partitioned postgres/mysql/redshift/clickhouse tables and then connect their APIs to read on those tables. Of course, this works fine except the fact that with an increased amount of intermediate-aggregated data, table indices grow and problems like cumulative sum or sorting become less and less efficient.
With your problem in hand, I think you can get a lot of help with AWS Lambda. AWS Lambda provides a very feasible serverless approach towards solving large granular data problems (if used correctly). For instance, assume that your pipelines partitions incoming stream by YYYYMMMDDHHMM and stores it into some S3 path which has a Lambda listening to it (as a trigger function) then your data ingest + aggregation becomes pretty much simultaneous processes. As soon as a minute is up, a new instance of the same Lambda function will be taking care of data landing into partition YYYYMMMDDHHMM+1. So, this way, you can run thousands of simultaneous processes with a good bunch of Lambda functions doing the same thing in parallel. Of course, this is a rough picture, but I think it can greatly help.
I am designing an application for which input is a large text file (size ranges from 1-30 GB) uploaded to S3 bucket every 15 min. It splits the file into n no of small ones and copy these files to 3 different S3 buckets in 3 different aws regions. Then 3 loader applications read these n files from respective s3 buckets and load the data into respective aerospike cluster.
I am thinking to use AWS lambda function to split the file as well as to load the data. I recently came across AWS step function which can also serve the purpose based on what I read. I am not sure which one to go with and which will be cheaper in terms of pricing. Any help is appreciated.
Thanks in advance!
Lambda and Step functions are like floors and steps to each floor. You cannot replace one with another.
Lambda is computing, steps functions take them to the desired step.
Youtube video explains very well: https://www.youtube.com/watch?v=Dh7h3lkpeP4
To the analogy again, you can have multiple computes (lambda) in a single floor before you pass it on the next floor.
One of the example is as shown below.
Usecase: https://john.soban.ski/transcribe-customer-service-voicemails-and-alert-on-keywords.html
Hope it helps.
Step functions are excellent at coordinating workflows that involve multiple predefined steps. It can do parallel tasks and error handling well. It mainly uses Lambda functions to perform each task.
Based on your use-case, step functions sound like a good fit. As far as pricing, it adds a very small additional charge on top of Lambdas. Based on your description, I doubt you'd even notice the additional cost. You'd need to evaluate that based on the number of "state transitions" you would be using. Of course, you'll also have to pay for your Lambda invocations.
So I want to dump an entire DynamoDB table to S3. This tutorial gives a good explanation of how to do so. Gave it a test, it worked...great
However now I want to use it on my production data which is sizeable (>100GB). And I want it to run quickly. Obviously the read throughput on my DynamoDB table is a factor here, but is there a way to make sure the data pipeline is doing everything it can. I'm not super familiar with these, the architect view after the setup has areas for instance types and instance count, but will increasing these decrease my pipeline time? The tutorial doesn't mention anything about speed except for specifying the throughput of the table you meant to use. Will it scale automatically based upon that?
The template is based on the open source samples that the datapipeline team has on gihub.
The template you are referring to is here .
If you take a look at the pipeline definition, you will find that the export is being done via a map-reduce job. The scalability of the export job should be handled by that.
If you need to get a few more details on how EMR works with DynamoDB, you will find it at the here. If you increase the number of instances you would need to adjust the throughput of your table accordingly to increase the parallelism of the export.