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I am using a nested ordered dictionary called toolSystem to store tools into categories and subtypes. The toolSystem.add is adding the categories and the gardeningTools.Add are adding the subtypes for that categorie (eg toolSystem has the categorie gardeningTools and a subtype of lineTrimer which is array of tools class).

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I am using a regex pattern to add QtyOrd before a number if it contains any quantity identifier. My code is working its the quantity is an integer. But for float number after using regex it creates a space after the first number. For e.g. 1.00 converted to 1 .00. it should not have an space

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I have a dataframe where I have a list of Clients and Categories, Products and the Department that has conducted outreach:

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I can use MongoDB with FastAPI either

1. with a global client: motor.motor_asyncio.AsyncIOMotorClient object, or else
2. by creating one during the startup event per this SO answer which refers to this "Real World Example".

However, I also want to use fastapi-users since it works nicely with MongoDB out of the box. The downside is it seems to only work with the first method of handling my DB client connection (ie global). The reason is that in order to configure fastapi-users, I have to have an active MongoDB client connection just so I can make the db object as shown below, and I need that db to then make the MongoDBUserDatabase object required by fastapi-users:

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I have a list of parts that have a part code. I need to align columns B-E to match the list of numbers in column A, leaving blanks where the data has moved down. The number in column B should match the number in column A.

A simple sort will not do because ColumnB,D,E has fewer entries than ColumnA and some numbers in ColumnB are not in ColumnA.

A B C D E '005023 5025 oil-filler-level-plug-genuine-005025 GENUINE PIAGGIO, OIL FILLER PLUG. 1.5 '005024 5027 rear-hub-cone-shim-lambretta-005027 LAMBRETTA REAR HUB CONE SHIM. 1.25 '005025 5031 piston-s2-s3-524mm-125cc-gol-005031 ITALIAN MADE BY GOL 46.5 '005027 5032 exhaust-simonini-px-125-black-005032 135 '005029 5036 floor-runner-kit-vespa-px-125-200-005036 GOOD QUALITY, ITALIAN MADE, COMLETE FLOOR RUNNER KIT 25 '005031 5037 rear-light-grey-top-for-vespa-rally-005037 5 '005032 5038 front-hub-back-plate-chrome-005038 Suitable for all Lambretta S1 S2 S3 models 45 '005033 5041 clutch-plates-surflex-cosa-vespa-px-005041 TOP QUALITY ITALIAN COSA CLUTCH PLATES MADE BY SURFLEX. 16 '005036 5044 points-ducati-style-lambretta-005044 TOP QUALITY,CONTACT BREAKER POINT FOR LAMBRETTA 10 '005037 5045 condensor-ducati-dansi-li-sx-tv-gp-005045 DUCATI TYPE CONDENSOR FOR MOST LAMBRETTAS. 9 '005038 5047 panel-handle-lock-mechanisms-s1-s2-005047 TOP QUALITY, LAMBRETTA SERIES 1 & 2 SIDE PANEL HANDLE MECHANISM KIT. 41 '005040 5049 fork-push-rods-pistons-s1-2-3-005049 TOP QUALITY LAMBRETTA FORK PUSH ROD PISTON SET. 12 '005041 5050 fuel-tank-vespa-gs-160-180ss-rally-005050 100 '005044 5051 wheel-rim-chrome-10-inch-vespa-005051 TOP QUALITY, CHROMED WHEEL RIMS ( 1 X WHEEL ) 38 '005045 5052 carb-box-top-carbon-look-pe-px-efl-005052 VBB SPRINT GT PX 22 '005047 5054 input-shaft-needle-rollers-px-21-005054 ITALIAN MADE SET OF 23 INPUT SHAFT NEEDLE ROLLER BEARINGS 5 '005049 5055 air-hose-clips-19mm-series-2-carb-005055 LAMBRETTA SERIES1 AND 2 AIR HOSE CLIPS FOR STANDARD 5 '005050 5056 air-hose-vespa-vna-005056 6.5 ...

I've been struggling with the following Mongo document:

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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)

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I'm working on a project where a Raspberry Pi controls some 12v pumps to eventually make cocktails. This is running in Flask, on a local Webserver (the Pi). There are multiple liquor bottles with hoses coupled to the pumps and the pumps are controlled via the GPIO pins on the Pi. This all works pretty well.

I want to add a function that prevents me from making a cocktail if the capacity of liquor that's left in the concerning bottle is insufficient. I've chosen to make a .JSON file as it is lightweight and fits my needs. An object in my .JSON file looks like this:

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I have a function in which I am trying to insert the users order into a order details table, where the admin user can see the order details of each order. the problem is, in my function, the same item is being inserted into the database twice, instead of each item. so it basically takes the last item ordered and inserts it. here's my function:

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