hose | A real-time resizing image server for Amazon S3 | Cloud Storage library

 by   linyows JavaScript Version: Current License: Non-SPDX

kandi X-RAY | hose Summary

kandi X-RAY | hose Summary

hose is a JavaScript library typically used in Storage, Cloud Storage, Nodejs, Wordpress, Amazon S3 applications. hose has no bugs, it has no vulnerabilities and it has low support. However hose has a Non-SPDX License. You can download it from GitHub.

A real-time resizing image server for Amazon S3.
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              hose has a low active ecosystem.
              It has 38 star(s) with 3 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of hose is current.

            kandi-Quality Quality

              hose has 0 bugs and 0 code smells.

            kandi-Security Security

              hose has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              hose code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              hose has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              hose releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.
              hose saves you 0 person hours of effort in developing the same functionality from scratch.
              It has 3 lines of code, 0 functions and 12 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

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            hose Key Features

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            hose Examples and Code Snippets

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            Community Discussions

            QUESTION

            No suitable constructor when trying to create instance
            Asked 2021-Jun-14 at 21:26

            Trying to setup a .net 5 console app with dependency injection and make use of a method in a class library. Not sure what Ive hosed up, but I get an exception

            'A suitable constructor for type 'TesterUtil.DataHelper.IBookMgr' could not be located. Ensure the type is concrete and services are registered for all parameters of a public constructor.'

            Main class

            ...

            ANSWER

            Answered 2021-Jun-14 at 21:26

            Resolve the desired type directly from the host's service provider,

            Source https://stackoverflow.com/questions/67977091

            QUESTION

            How can I call a method on an element in a nested OrderedDictionary by index
            Asked 2021-May-20 at 13:49

            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).

            ...

            ANSWER

            Answered 2021-May-20 at 13:49

            You were close with your attempt to use the strings to access the elements in the dictionaries, with typed collections this sort of syntax will work, but the issue is that the OrderedDictionary is untyped, so the value of the elements is typed as an Object so you will need to cast it in order to use the specific indexer logic:

            The following is a simple attempt that explicitly casts the individual elements to the types that we assume that they are:

            Source https://stackoverflow.com/questions/67600391

            QUESTION

            Space have created after using regex for foloat number in python
            Asked 2021-May-12 at 10:30

            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

            ...

            ANSWER

            Answered 2021-May-07 at 09:53

            Remove spaces after \3 in r' \1 QtyOrd \3 '

            Source https://stackoverflow.com/questions/67432597

            QUESTION

            In R, find adjacent rows given a particular column
            Asked 2021-Apr-30 at 20:34

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

            ...

            ANSWER

            Answered 2021-Apr-14 at 18:32

            Here's an approach that adds the number of Marketing and Sales outreaches within each Client-Category group, then filters for ones where Sales contacted but Marketing did not.

            Source https://stackoverflow.com/questions/67097123

            QUESTION

            How can I use FastAPI Routers with FastAPI-Users and MongoDB?
            Asked 2021-Mar-21 at 10:50

            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:

            ...

            ANSWER

            Answered 2021-Mar-13 at 18:56

            I don't think my solution is complete or correct, but I figured I'd post it in case it inspires any ideas, I'm stumped. I have run into the exact dilemma, almost seems like a design flaw..

            I followed this MongoDB full example and named it main.py

            At this point my app does not work. The server starts up but result results in the aforementioned "attached to a different loop" whenever trying to query the DB.

            Looking for guidance, I stumbled upon the same "real world" example

            In main.py added the startup and shudown event handlers

            Source https://stackoverflow.com/questions/65930477

            QUESTION

            Moving cell data to respective row based on numbers in another column
            Asked 2021-Mar-10 at 22:52

            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 ...

            ANSWER

            Answered 2021-Mar-10 at 22:52

            Add a reference from the VBA editor (Tools -> References...) to Microsoft ActiveX Data Objects; choose the latest version, usually 6.1

            Then you could write VBA code like the following:

            Source https://stackoverflow.com/questions/66530748

            QUESTION

            Find Object within an Array that is within an object also within an Array
            Asked 2021-Mar-05 at 19:02

            I've been struggling with the following Mongo document:

            ...

            ANSWER

            Answered 2021-Mar-05 at 19:02

            QUESTION

            How to efficiently aggregate data in billions of individual records in AWS?
            Asked 2021-Feb-17 at 20:48

            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)

            ...

            ANSWER

            Answered 2021-Jan-07 at 18:45

            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:

            1. Fix your object size (Kineses can be configured to do this)
            2. Partition your data by day
            3. Set up a small Redshift cluster (4 X dc2.large) and use Spectrum source address the data
            4. Connect Quicksight to Redshift
            5. Measure the performance (and cost) and compare to requirements (there will likely be gaps)
            6. 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.

            Source https://stackoverflow.com/questions/65603353

            QUESTION

            How do I change a value of a JSON object with Python?
            Asked 2021-Jan-11 at 21:38

            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:

            ...

            ANSWER

            Answered 2021-Jan-11 at 21:38

            Did you mean to update the fullness?

            Source https://stackoverflow.com/questions/65674436

            QUESTION

            inserting same value twice into db nodejs
            Asked 2020-Dec-30 at 17:10

            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:

            ...

            ANSWER

            Answered 2020-Dec-30 at 17:10

            You don't need to open connection again in loop.

            Source https://stackoverflow.com/questions/65510773

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            Vulnerabilities

            No vulnerabilities reported

            Install hose

            Deploy file on "bucketName":.

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