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seldo · 6 years ago

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Databases: how they work, and a brief history

My twitter-friend Simon had a simple question that contained much complexity: how do databases work?

Ok, so databases really confuse me, like how do databases even work?

— Simon Legg (@simonleggsays) November 18, 2019

I don't have a job at the moment, and I really love databases and also teaching things to web developers, so this was a perfect storm for me:

To what level of detail would you like an answer? I love databases.

— Laurie Voss (@seldo) November 18, 2019

The result was an absurdly long thread of 70+ tweets, in which I expounded on the workings and history of databases as used by modern web developers, and Simon chimed in on each tweet with further questions and requests for clarification. The result of this collaboration was a super fun tiny explanation of databases which many people said they liked, so here it is, lightly edited for clarity.

What is a database?

Let's start at the very most basic thing, the words we're using: a "database" literally just means "a structured collection of data". Almost anything meets this definition – an object in memory, an XML file, a list in HTML. It's super broad, so we call some radically different things "databases".

The thing people use all the time is, formally, a Database Management System, abbreviated to DBMS. This is a piece of software that handles access to the pile of data. Technically one DBMS can manage multiple databases (MySQL and postgres both do this) but often a DBMS will have just one database in it.

Because it's so frequent that the DBMS has one DB in it we often call a DBMS a "database". So part of the confusion around databases for people new to them is because we call so many things the same word! But it doesn't really matter, you can call an DBMS a "database" and everyone will know what you mean. MySQL, Redis, Postgres, RedShift, Oracle etc. are all DBMS.

So now we have a mental model of a "database", really a DBMS: it is a piece of software that manages access to a pile of structured data for you. DBMSes are often written in C or C++, but it can be any programming language; there are databases written in Erlang and JavaScript. One of the key differences between DBMSes is how they structure the data.

Relational databases

Relational databases, also called RDBMS, model data as a table, like you'd see in a spreadsheet. On disk this can be as simple as comma-separated values: one row per line, commas between columns, e.g. a classic example is a table of fruits:

apple,10,5.00 orange,5,6.50

The DBMS knows the first column is the name, the second is the number of fruits, the third is the price. Sometimes it will store that information in a different database! Sometimes the metadata about what the columns are will be in the database file itself. Because it knows about the columns, it can handle niceties for you: for example, the first column is a string, the second is an integer, the third is dollar values. It can use that to make sure it returns those columns to you correctly formatted, and it can also store numbers more efficiently than just strings of digits.

In reality a modern database is doing a whole bunch of far more clever optimizations than just comma separated values but it's a mental model of what's going on that works fine. The data all lives on disk, often as one big file, and the DBMS caches parts of it in memory for speed. Sometimes it has different files for the data and the metadata, or for indexes that make it easier to find things quickly, but we can safely ignore those details.

RDBMS are older, so they date from a time when memory was really expensive, so they usually optimize for keeping most things on disk and only put some stuff in memory. But they don't have to: some RDBMS keep everything in memory and never write to disk. That makes them much faster!

Is it still a database if all the structured data stays in memory? Sure. It's a pile of structured data. Nothing in that definition says a disk needs to be involved.

So what does the "relational" part of RDBMS mean? RDBMS have multiple tables of data, and they can relate different tables to each other. For instance, imagine a new table called "Farmers":

IDName 1bob 2susan

and we modify the Fruits table:

Farmer IDFruitQuantityPrice 1apple105.00 1orange56.50 2apple206.00 2orange14.75

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The Farmers table gives each farmer a name and an ID. The Fruits table now has a column that gives the Farmer ID, so you can see which farmer has which fruit at which price.

Why's that helpful? Two reasons: space and time. Space because it reduces data duplication. Remember, these were invented when disks were expensive and slow! Storing the data this way lets you only list "susan" once no matter how many fruits she has. If she had a hundred kinds of fruit you'd be saving quite a lot of storage by not repeating her name over and over. The time reason comes in if you want to change Susan's name. If you repeated her name hundreds of times you would have to do a write to disk for each one (and writes were very slow at the time this was all designed). That would take a long time, plus there's a chance you could miss one somewhere and suddenly Susan would have two names and things would be confusing.

Relational databases make it easy to do certain kinds of queries. For instance, it's very efficient to find out how many fruits there are in total: you just add up all the numbers in the Quantity column in Fruits, and you never need to look at Farmers at all. It's efficient and because the DBMS knows where the data is you can say "give me the sum of the quantity colum" pretty simply in SQL, something like SELECT SUM(Quantity) FROM Fruits. The DBMS will do all the work.

NoSQL databases

So now let's look at the NoSQL databases. These were a much more recent invention, and the economics of computer hardware had changed: memory was a lot cheaper, disk space was absurdly cheap, processors were a lot faster, and programmers were very expensive. The designers of newer databases could make different trade-offs than the designers of RDBMS.

The first difference of NoSQL databases is that they mostly don't store things on disk, or do so only once in a while as a backup. This can be dangerous – if you lose power you can lose all your data – but often a backup from a few minutes or seconds ago is fine and the speed of memory is worth it. A database like Redis writes everything to disk every 200ms or so, which is hardly any time at all, while doing all the real work in memory.

A lot of the perceived performance advantages of "noSQL" databases is just because they keep everything in memory and memory is very fast and disks, even modern solid-state drives, are agonizingly slow by comparison. It's nothing to do with whether the database is relational or not-relational, and nothing at all to do with SQL.

But the other thing NoSQL database designers did was they abandoned the "relational" part of databases. Instead of the model of tables, they tended to model data as objects with keys. A good mental model of this is just JSON:

[ {"name":"bob"} {"name":"susan","age":55} ]

Again, just as a modern RDBMS is not really writing CSV files to disk but is doing wildly optimized stuff, a NoSQL database is not storing everything as a single giant JSON array in memory or disk, but you can mentally model it that way and you won't go far wrong. If I want the record for Bob I ask for ID 0, Susan is ID 1, etc..

One advantage here is that I don't need to plan in advance what I put in each record, I can just throw anything in there. It can be just a name, or a name and an age, or a gigantic object. With a relational DB you have to plan out columns in advance, and changing them later can be tricky and time-consuming.

Another advantage is that if I want to know everything about a farmer, it's all going to be there in one record: their name, their fruits, the prices, everything. In a relational DB that would be more complicated, because you'd have to query the farmers and fruits tables at the same time, a process called "joining" the tables. The SQL "JOIN" keyword is one way to do this.

One disadvantage of storing records as objects like this, formally called an "object store", is that if I want to know how many fruits there are in total, that's easy in an RDBMS but harder here. To sum the quantity of fruits, I have to retrieve each record, find the key for fruits, find all the fruits, find the key for quantity, and add these to a variable. The DBMS for the object store may have an API to do this for me if I've been consistent and made all the objects I stored look the same. But I don't have to do that, so there's a chance the quantities are stored in different places in different objects, making it quite annoying to get right. You often have to write code to do it.

But sometimes that's okay! Sometimes your app doesn't need to relate things across multiple records, it just wants all the data about a single key as fast as possible. Relational databases are best for the former, object stores the best for the latter, but both types can answer both types of questions.

Some of the optimizations I mentioned both types of DBMS use are to allow them to answer the kinds of questions they're otherwise bad at. RDBMS have "object" columns these days that let you store object-type things without adding and removing columns. Object stores frequently have "indexes" that you can set up to be able to find all the keys in a particular place so you can sum up things like Quantity or search for a specific Fruit name fast.

So what's the difference between an "object store" and a "noSQL" database? The first is a formal name for anything that stores structured data as objects (not tables). The second is... well, basically a marketing term. Let's digress into some tech history!

The self-defeating triumph of MySQL

Back in 1995, when the web boomed out of nowhere and suddenly everybody needed a database, databases were mostly commercial software, and expensive. To the rescue came MySQL, invented 1995, and Postgres, invented 1996. They were free! This was a radical idea and everybody adopted them, partly because nobody had any money back then – the whole idea of making money from websites was new and un-tested, there was no such thing as a multi-million dollar seed round. It was free or nothing.

The primary difference between PostgreSQL and MySQL was that Postgres was very good and had lots of features but was very hard to install on Windows (then, as now, the overwhelmingly most common development platform for web devs). MySQL did almost nothing but came with a super-easy installer for Windows. The result was MySQL completely ate Postgres' lunch for years in terms of market share.

Lots of database folks will dispute my assertion that the Windows installer is why MySQL won, or that MySQL won at all. But MySQL absolutely won, and it was because of the installer. MySQL became so popular it became synonymous with "database". You started any new web app by installing MySQL. Web hosting plans came with a MySQL database for free by default, and often no other databases were even available on cheaper hosts, which further accelerated MySQL's rise: defaults are powerful.

The result was people using mySQL for every fucking thing, even for things it was really bad at. For instance, because web devs move fast and change things they had to add new columns to tables all the time, and as I mentioned RDBMS are bad at that. People used MySQL to store uploaded image files, gigantic blobs of binary data that have no place in a DBMS of any kind.

People also ran into a lot of problems with RDBMS and MySQL in particular being optimized for saving memory and storing everything on disk. It made huge databases really slow, and meanwhile memory had got a lot cheaper. Putting tons of data in memory had become practical.

The rise of in-memory databases

The first software to really make use of how cheap memory had become was Memcache, released in 2003. You could run your ordinary RDBMS queries and just throw the results of frequent queries into Memcache, which stored them in memory so they were way, WAY faster to retrieve the second time. It was a revolution in performance, and it was an easy optimization to throw into your existing, RDBMS-based application.

By 2009 somebody realized that if you're just throwing everything in a cache anyway, why even bother having an RDBMS in the first place? Enter MongoDB and Redis, both released in 2009. To contrast themselves with the dominant "MySQL" they called themselves "NoSQL".

What's the difference between an in-memory cache like Memcache and an in-memory database like Redis or MongoDB? The answer is: basically nothing. Redis and Memcache are fundamentally almost identical, Redis just has much better mechanisms for retrieving and accessing the data in memory. A cache is a kind of DB, Memcache is a DBMS, it's just not as easy to do complex things with it as Redis.

Part of the reason Mongo and Redis called themselves NoSQL is because, well, they didn't support SQL. Relational databases let you use SQL to ask questions about relations across tables. Object stores just look up objects by their key most of the time, so the expressiveness of SQL is overkill. You can just make an API call like get(1) to get the record you want.

But this is where marketing became a problem. The NoSQL stores (being in memory) were a lot faster than the relational DBMS (which still mostly used disk). So people got the idea that SQL was the problem, that SQL was why RDBMS were slow. The name "NoSQL" didn't help! It sounded like getting rid of SQL was the point, rather than a side effect. But what most people liked about the NoSQL databases was the performance, and that was just because memory is faster than disk!

Of course, some people genuinely do hate SQL, and not having to use SQL was attractive to them. But if you've built applications of reasonable complexity on both an RDBMS and an object store you'll know that complicated queries are complicated whether you're using SQL or not. I have a lot of love for SQL.

If putting everything in memory makes your database faster, why can't you build an RDBMS that stores everything in memory? You can, and they exist! VoltDB is one example. They're nice! Also, MySQL and Postgres have kind of caught up to the idea that machines have lots more RAM now, so you can configure them to keep things mostly in memory too, so their default performance is a lot better and their performance after being tuned by an expert can be phenomenal.

So anything that's not a relational database is technically a "NoSQL" database. Most NoSQL databases are object stores but that's really just kind of a historical accident.

How does my app talk to a database?

Now we understand how a database works: it's software, running on a machine, managing data for you. How does your app talk to the database over a network and get answers to queries? Are all databases just a single machine?

The answer is: every DBMS, whether relational or object store, is a piece of software that runs on machine(s) that hold the data. There's massive variation: some run on 1 machine, some on clusters of 5-10, some run across thousands of separate machines all at once.

The DBMS software does the management of the data, in memory or on disk, and it presents an API that can be accessed locally, and also more importantly over the network. Sometimes this is a web API like you're used to, literally making GET and POST calls over HTTP to the database. For other databases, especially the older ones, it's a custom protocol.

Either way, you run a piece of software in your app, usually called a Client. That client knows the protocol for talking to the database, whether it's HTTP or WhateverDBProtocol. You tell it where the database server is on the network, it sends queries over and gets responses. Sometimes the queries are literally strings of text, like "SELECT * FROM Fruits", sometimes they are JSON payloads describing records, and any number of other variations.

As a starting point, you can think of the client running on your machine talking over the network to a database running on another machine. Sometimes your app is on dozens of machines, and the database is a single IP address with thousands of machines pretending to be one machine. But it works pretty much the same either way.

The way you tell your client "where" the DB is is your connection credentials, often expressed as a string like "http://username:[email protected]:1234" or "mongodb://...". But this is just a convenient shorthand. All your client really needs to talk to a database is the DNS name (like mydb.com) or an IP address (like 205.195.134.39), plus a port (1234). This tells the network which machine to send the query to, and what "door" to knock on when it gets there.

A little about ports: machines listen on specific ports for things, so if you send something to port 80, the machine knows the query is for your web server, but if you send it to port 1234, it knows the query is for your database. Who picks 1234 (In the case of Postgres, it's literally 5432)? There's no rhyme or reason to it. The developers pick a number that's easy to remember between 1 and 65,535 (the highest port number available) and hope that no other popular piece of software is already using it.

Usually you'll also have a username and password to connect to the database, because otherwise anybody who found your machine could connect to your database and get all the data in it. Forgetting that this is true is a really common source of security breaches!

There are bad people on the internet who literally just try every single IP in the world and send data to the default port for common databases and try to connect without a username or password to see if they can. If it works, they take all the data and then ransom it off. Yikes! Always make sure your database has a password.

Of course, sometimes you don't talk to your database over a network. Sometimes your app and your database live on the same machine. This is common in desktop software but very rare in web apps. If you've ever heard of a "database driver", the "driver" is the equivalent of the "client", but for talking to a local database instead of over a network.

Replication and scaling

Remember I said some databases run on just 1 machine, and some run on thousands of machines? That's known as replication. If you have more than one copy of a piece of data, you have a "replica" of that data, hence the name.

Back in the old days hardware was expensive so it was unusual to have replicas of your data running at the same time. It was expensive. Instead you'd back up your data to tape or something, and if the database went down because the hardware wore out or something, then you'd buy new hardware and (hopefully) reinstall your DBMS and restore the data in a few hours.

Web apps radically changed people's demands of databases. Before web apps, most databases weren't being continuously queried by the public, just a few experts inside normal working hours, and they would wait patiently if the database broke. With a web app you can't have minutes of downtime, far less hours, so replication went from being a rare feature of expensive databases to pretty much table stakes for every database. The initial form of replication was a "hot spare".

If you ran a hot spare, you'd have your main DBMS machine, which handled all queries, and a replica DBMS machine that would copy every single change that happened on the primary to itself. Primary was called m****r and the replica s***e because the latter did whatever the former told it to do, and at the time nobody considered how horrifying that analogy was. These days we call those things "primary/secondary" or "primary/replica" or for more complicated arrangements things like "root/branch/leaf".

Sometimes, people would think having a hot spare meant they didn't need a backup. This is a huge mistake! Remember, the replica copies every change in the main database. So if you accidentally run a command that deletes all the data in your primary database, it will automatically delete all the data in the replica too. Replicas are not backups, as the bookmarking site Magnolia famously learned.

People soon realized having a whole replica machine sitting around doing nothing was a waste, so to be more efficient they changed where traffic went: all the writes would go to the primary, which would copy everything to the replicas, and all the reads would go to the replicas. This was great for scale!

Instead of having 1 machine worth of performance (and you could swap to the hot spare if it failed, and still have 1 machine of performance with no downtime) suddenly you had X machines of performance, where X could be dozens or even hundreds. Very helpful!

But primary/secondary replication of this kind has two drawbacks. First, if a write has arrived at the primary database but not yet replicated to all the secondary machines (which can take half a second if the machines are far apart or overloaded) then somebody reading from the replica can get an answer that's out of date. This is known as a "consistency" failure, and we'll talk about it more later.

The second flaw with primary/second replication is if the primary fails, suddenly you can no longer write to your database. To restore the ability to do writes, you have to take one of the replicas and "promote" it to primary, and change all the other replicas to point at this new primary box. It's time-consuming and notoriously error-prone.

So newer databases invented different ways of arranging the machines, formally called "network topology". If you think of the way machines connect to each other as a diagram, the topology is the shape of that diagram. Primary/secondary looks like a star. Root/branch/leaf looks like a tree. But you can have a ring structure, or a mesh structure, or lots of others. A mesh structure is a lot of fun and very popular, so let's talk about more about them.

Mesh replication databases

In a mesh structure, every machine is talking to every other machine and they all have some portion of the data. You can send a write to any machine and it will either store it, or figure out what machine should store it and send it to that machine. Likewise, you can query any machine in the mesh, and it will give you the answer if it has the data, or forward your request to a machine that does. There's no "primary" machine to fail. Neat!

Because each machine can get away with storing only some of the data and not all of it, a mesh database can store much, much more data than a single machine could store. If 1 machine could store X data, then N machines could theoretically store N*X data. You can almost scale infinitely that way! It's very cool.

Of course, if each record only existed on one machine, then if that machine failed you'd lose those records. So usually in a mesh network more than one machine will have a copy of any individual record. That means you can lose machines without losing data or experiencing downtime; there are other copies lying around. In some mesh databases can also add a new machine to the mesh and the others will notice it and "rebalance" data, increasing the capacity of the database without any downtime. Super cool.

So a mesh topology is a lot more complicated but more resilient, and you can scale it without having to take the database down (usually). This is very nice, but can go horribly wrong if, for instance, there's a network error and suddenly half the machines can't see the other half of the machines in the mesh. This is called a "network partition" and it's a super common failure in large networks. Usually a partition will last only a couple of seconds but that's more than enough to fuck up a database. We'll talk about network partitions shortly.

One important question about a mesh DB is: how do you connect to it? Your client needs to know an IP address to connect to a database. Does it need to know the IP addresses of every machine in the mesh? And what happens when you add and remove machines from the mesh? Sounds messy.

Different Mesh DBs do it differently, but usually you get a load balancer, another machine that accepts all the incoming connections and works out which machine in the mesh should get the question and hands it off. Of course, this means the load balancer can fail, hosing your DB. So usually you'll do some kind of DNS/IP trickery where there are a handful of load balancers all responding on the same domain name or IP address.

The end result is your client magically just needs to know only one name or IP, and that IP always responds because the load balancer always sends you to a working machine.

CAP theory

This brings us neatly to a computer science term often used to talk about databases which is Consistency, Availability, and Partition tolerance, aka CAP or "CAP theory". The basic rule of CAP theory is: you can't have all 3 of Consistency, Availability and Partition Tolerance at the same time. Not because we're not smart enough to build a database that good, but because doing so violates physics.

Consistency means, formally: every query gets the correct, most up-to-date answer (or an error response saying you can't have it).

Availability means: every query gets an answer (but it's not guaranteed to be the correct one).

Partition Tolerance means: if the network craps out, the database will continue to work.

You can already see how these conflict! If you're 100% Available it means by definition you'll never give an error response, so sometimes the data will be out of date, i.e. not Consistent. If your database is Partition Tolerant, on the other hand, it keeps working even if machine A can't talk to machine B, and machine A might have a more recent write than B, so machine B will give stale (i.e. not Consistent) responses to keep working.

So let's think about how CAP theorem applies across the topologies we already talked about.

A single DB on a single machine is definitely Consistent (there's only one copy of the data) and Partition Tolerant (there's no network inside of it to crap out) but not Available because the machine itself can fail, e.g. the hardware could literally break or power could go out.

A primary DB with several replicas is Available (if one replica fails you can ask another) and Partition Tolerant (the replicas will respond even if they're not receiving writes from the primary) but not Consistent (because as mentioned earlier, the replicas might not have every primary write yet).

A mesh DB is extremely Available (all the nodes always answer) and Partition Tolerant (just try to knock it over! It's delightfully robust!) but can be extremely inconsistent because two different machines on the mesh could get a write to the same record at the same time and fight about which one is "correct".

This is the big disadvantage to mesh DBs, which otherwise are wonderful. Sometimes it's impossible to know which of two simultaneous writes is the "winner". There's no single authority, and Very Very Complicated Algorithms are deployed trying to prevent fights breaking out between machines in the mesh about this, with highly variable levels of success and gigantic levels of pain when they inevitably fail. You can't get all three of CAP and Consistency is what mesh networks lose.

In all databases, CAP isn't a set of switches where you are or aren't Consistent, Available, or Partition Tolerant. It's more like a set of sliders. Sliding up the Partition Tolerance generally slides down Consistency, sliding down Availability will give you more Consistency, etc etc.. Every DBMS picks some combination of CAP and picking the right database is often a matter of choosing what CAP combination is appropriate for your application.

Other topologies

Some other terms you frequently hear in the world of databases are "partitions" (which are different from the network partitions of CAP theorem) and "shards". These are both additional topologies available to somebody designing a database. Let's talk about shards first.

Imagine a primary with multiple replicas, but instead of each replica having all the data, each replica has a slice (or shard) of the data. You can slice the data lots of ways. If the database was people, you could have 26 shards, one with all names starting with A, one with all the names starting with B, etc..

Sharding can be helpful if the data is too big to all fit on one disk at a time. This is less of a problem than it used to be because virtual machines these days can effectively have infinity-sized hard drives.

The disadvantage of sharding is it's less Available: if you lose a shard, you lose everybody who starts with that letter! (Of course, your shards can also have replicas...) Plus your software needs to know where all the shards are and which one to ask a question. It's fiddly. Many of the problems of sharded databases are solved by using mesh topologies instead.

Partitions are another way of splitting up a database, but instead of splitting it across many machines, it splits the database across many files in a single machine. This is an old pattern that was useful when you had really powerful hardware and really slow disks, because you could install multiple disks into a single machine and put different partitions on each one, speeding up your achingly slow, disk-based database. These days there's not a lot of reason to use partitions of this kind.

Fin

That concludes this impromptu Databases 101 seminar! I hope you enjoyed learning a little bit more about this fantastically fun and critically important genre of software. from Seldo.Com Feed https://ift.tt/32XwZth

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loadingtax915 · 4 years ago

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Python Docx

Python Docx4j

Python Docx To Pdf

Python Docx Table

Python Docx To Pdf

Python Docx2txt

When you ask someone to send you a contract or a report there is a high probability that you’ll get a DOCX file. Whether you like it not, it makes sense considering that 1.2 billion people use Microsoft Office although a definition of “use” is quite vague in this case. DOCX is a binary file which is, unlike XLSX, not famous for being easy to integrate into your application. PDF is much easier when you care more about how a document is displayed than its abilities for further modifications. Let’s focus on that.

Python-docx versions 0.3.0 and later are not API-compatible with prior versions. Python-docx is hosted on PyPI, so installation is relatively simple, and just depends on what installation utilities you have installed. Python-docx may be installed with pip if you have it available.

Installing Python-Docx Library Several libraries exist that can be used to read and write MS Word files in Python. However, we will be using the python-docx module owing to its ease-of-use. Execute the following pip command in your terminal to download the python-docx module as shown below.

Python has a few great libraries to work with DOCX (python-dox) and PDF files (PyPDF2, pdfrw). Those are good choices and a lot of fun to read or write files. That said, I know I'd fail miserably trying to achieve 1:1 conversion.

Release v0.8.10 (Installation)python-docx is a Python library for creating and updating Microsoft Word (.docx) files.

Looking further I came across unoconv. Universal Office Converter is a library that’s converting any document format supported by LibreOffice/OpenOffice. That sound like a solid solution for my use case where I care more about quality than anything else. As execution time isn't my problem I have been only concerned whether it’s possible to run LibreOffice without X display. Apparently, LibreOffice can be run in haedless mode and supports conversion between various formats, sweet!

I’m grateful to unoconv for an idea and great README explaining multiple problems I can come across. In the same time, I’m put off by the number of open issues and abandoned pull requests. If I get versions right, how hard can it be? Not hard at all, with few caveats though.

Testing converter

LibreOffice is available on all major platforms and has an active community. It's not active as new-hot-js-framework-active but still with plenty of good read and support. You can get your copy from the download page. Be a good user and go with up-to-date version. You can always downgrade in case of any problems and feedback on latest release is always appreciated.

On macOS and Windows executable is called soffice and libreoffice on Linux. I'm on macOS, executable soffice isn't available in my PATH after the installation but you can find it inside the LibreOffice.app. To test how LibreOffice deals with your files you can run:

In my case results were more than satisfying. The only problem I saw was a misalignment in a file when the alignment was done with spaces, sad but true. This problem was caused by missing fonts and different width of 'replacements' fonts. No worries, we'll address this problem later.

Setup I

While reading unoconv issues I've noticed that many problems are connected due to the mismatch of the versions. I'm going with Docker so I can have pretty stable setup and so I can be sure that everything works.

Let's start with defining simple Dockerfile, just with dependencies and ADD one DOCX file just for testing:

Let's build an image:

After image is created we can run the container and convert the file inside the container:

Running LibreOffice as a subprocess

We want to run LibreOffice converter as a subprocess and provide the same API for all platforms. Let's define a module which can be run as a standalone script or which we can later import on our server.

Required arguments which convert_to accepts are folder to which we save PDF and a path to the source file. Optionally we specify a timeout in seconds. I’m saying optional but consider it mandatory. We don’t want a process to hang too long in case of any problems or just to limit computation time we are able to give away to each conversion. LibreOffice executable location and name depends on the platform so edit libreoffice_exec to support platform you’re using.

subprocess.run doesn’t capture stdout and stderr by default. We can easily change the default behavior by passing subprocess.PIPE. Unfortunately, in the case of the failure, LibreOffice will fail with return code 0 and nothing will be written to stderr. I decided to look for the success message assuming that it won’t be there in case of an error and raise LibreOfficeError otherwise. This approach hasn’t failed me so far.

Uploading files with Flask

Converting using the command line is ok for testing and development but won't take us far. Let's build a simple server in Flask.

We'll need few helper function to work with files and few custom errors for handling error messages. Upload directory path is defined in config.py. You can also consider using flask-restplus or flask-restful which makes handling errors a little easier.

The server is pretty straightforward. In production, you would probably want to use some kind of authentication to limit access to uploads directory. If not, give up on serving static files with Flask and go for Nginx.

Important take-away from this example is that you want to tell your app to be threaded so one request won't prevent other routes from being served. However, WSGI server included with Flask is not production ready and focuses on development. In production, you want to use a proper server with automatic worker process management like gunicorn. Check the docs for an example how to integrate gunicorn into your app. We are going to run the application inside a container so host has to be set to publicly visible 0.0.0.0.

Setup II

Now when we have a server we can update Dockerfile. We need to copy our application source code to the image filesystem and install required dependencies.

In docker-compose.yml we want to specify ports mapping and mount a volume. If you followed the code and you tried running examples you have probably noticed that we were missing the way to tell Flask to run in a debugging mode. Defining environment variable without a value is causing that this variable is going to be passed to the container from the host system. Alternatively, you can provide different config files for different environments.

Supporting custom fonts

I've mentioned a problem with missing fonts earlier. LibreOffice can, of course, make use of custom fonts. If you can predict which fonts your user might be using there's a simple remedy. Add following line to your Dockfile.

Now when you put custom font file in the font directory in your project, rebuild the image. From now on you support custom fonts!

Summary

This should give you the idea how you can provide quality conversion of different documents to PDF. Although the main goal was to convert a DOCX file you should be fine with presentations, spreadsheets or images.

Further improvements could be providing support for multiple files, the converter can be configured to accept more than one file as well.

Photo by Samuel Zeller on Unsplash.

Did you enjoy it? Follow me@MichalZalecki on Twitter, where I share some interesting, bite-size content.

This ebook goes beyond Jest documentation to explain software testing techniques. I focus on unit test separation, mocking, matchers, patterns, and best practices.

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Latest version

Released:

Extract content from docx files

Project description

Extract docx headers, footers, text, footnotes, endnotes, properties, and images to a Python object.

shared features:

extracts text from docx files

extracts images from docx files

no dependencies (docx2python requires pytest to test)

additions:

extracts footnotes and endnotes

converts bullets and numbered lists to ascii with indentation

converts hyperlinks to <a href='http:/...'>link text</a>

retains some structure of the original file (more below)

extracts document properties (creator, lastModifiedBy, etc.)

inserts image placeholders in text ('----image1.jpg----')

inserts plain text footnote and endnote references in text ('----footnote1----')

(optionally) retains font size, font color, bold, italics, and underscore as html

extract user selections from checkboxes and dropdown menus

full test coverage and documentation for developers

subtractions:

no command-line interface

will only work with Python 3.4+

Installation

Use

Note on html feature:

font size, font color, bold, italics, and underline supported

hyperlinks will always be exported as html (<a href='http:/...'>link text</a>), even if export_font_style=False, because I couldn't think of a more cononical representation.

every tag open in a paragraph will be closed in that paragraph (and, where appropriate, reopened in the next paragraph). If two subsequenct paragraphs are bold, they will be returned as <b>paragraph q</b>, <b>paragraph 2</b>. This is intentional to make each paragraph its own entity.

if you specify export_font_style=True, > and < in your docx text will be encoded as > and <

Return Value

Function docx2python returns an object with several attributes.

header - contents of the docx headers in the return format described herein

footer - contents of the docx footers in the return format described herein

body - contents of the docx in the return format described herein

footnotes - contents of the docx in the return format described herein

endnotes - contents of the docx in the return format described herein

document - header + body + footer (read only)

text - all docx text as one string, similar to what you'd get from python-docx2txt

properties - docx property names mapped to values (e.g., {'lastModifiedBy': 'Shay Hill'})

images - image names mapped to images in binary format. Write to filesystem with

Return Format

Some structure will be maintained. Text will be returned in a nested list, with paragraphs always at depth 4 (i.e., output.body[i][j][k][l] will be a paragraph).

If your docx has no tables, output.body will appear as one a table with all contents in one cell:

Table cells will appear as table cells. Text outside tables will appear as table cells.

To preserve the even depth (text always at depth 4), nested tables will appear as new, top-level tables. This is clearer with an example:

becomes ...

This ensures text appears

only once

in the order it appears in the docx

always at depth four (i.e., result.body[i][j][k][l] will be a string).

Working with output

This package provides several documented helper functions in the docx2python.iterators module. Here are a few recipes possible with these functions:

Some fine print about checkboxes:

MS Word has checkboxes that can be checked any time, and others that can only be checked when the form is locked.The previous print as. u2610 (open checkbox) or u2612 (crossed checkbox). Which this module, the latter willtoo. I gave checkboxes a bailout value of ----checkbox failed---- if the xml doesn't look like I expect it to,because I don't have several-thousand test files with checkboxes (as I did with most of the other form elements).Checkboxes should work, but please let me know if you encounter any that do not.

Release historyRelease notifications | RSS feed

1.27.1

1.27

1.26

Python Docx4j

1.25

1.24

1.23

1.22

1.21

1.19

1.18

1.17

1.16

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1.13

1.12

1.11

1.2

Python Docx To Pdf

1.1

Python Docx Table

1.0

0.1

Python Docx To Pdf

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Python Docx2txt

Files for docx2python, version 1.27.1Filename, sizeFile typePython versionUpload dateHashesFilename, size docx2python-1.27.1-py3-none-any.whl (22.9 kB) File type Wheel Python version py3 Upload dateHashesFilename, size docx2python-1.27.1.tar.gz (33.3 kB) File type Source Python version None Upload dateHashes

Hashes for docx2python-1.27.1-py3-none-any.whl

Hashes for docx2python-1.27.1-py3-none-any.whlAlgorithmHash digestSHA25651f6f03149efff07372ea023824d4fd863cb70b531aa558513070fe60f1c420aMD54b0ee20fed4a8cb0eaba8580c33f946bBLAKE2-256e7d5ff32d733592b17310193280786c1cab22ca4738daa97e1825d650f55157c

Hashes for docx2python-1.27.1.tar.gz

Python Docx2txt

Hashes for docx2python-1.27.1.tar.gzAlgorithmHash digestSHA2566ca0a92ee9220708060ece485cede894408588353dc458ee5ec17959488fa668MD5759e1630c6990533414192eb57333c72BLAKE2-25684783b70aec51652a4ec4f42aa419a8af18d967b06390764527c81f183d1c02a

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t-baba · 5 years ago

Photo

🎉 JavaScript turns 25 years old

#489 — May 22, 2020

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A Complete Walkthrough to Using WebGL — A really thorough walkthrough of getting started with WebGL at the low level, complete with integrated, editable examples and coverage of the math behind 3D rendering. If you’ve ever wondered what libraries like Three.js are using behind the scenes, it’s all here.

Maxime Euzière

Microsoft Unveils 'Azure Static Web Apps' — Azure Static Web Apps brings modern static site deployment to Azure and integrates with GitHub and Visual Studio Code too. Want to see more? Here’s a 6 minute screencast demo. Yet another way to deploy those static single page apps :-)

Microsoft

The Most Complete Spreadsheet for JavaScript Apps – SpreadJS — Deliver true Excel-like experiences with this fast JavaScript enterprise spreadsheet solution. Build FinTech, analysis, budgeting, and forecasting apps. Featuring an Excel I/O, 450+ functions, tables, charts, sparklines, and more. View the latest demo.

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The Unreasonable Effectiveness of Declarative Programming — Siddharth shows off his single file animation library by way of showing off the benefits of doing things in an (arguably) declarative style. A nifty post, this, which encourages you to interact with the code yourself.

Siddharth Bhat

The Third Age of JavaScript? — Yes, purely an opinion piece but he might have a point. “Every 10 years there is a changing of the guard in JavaScript. I think we have just started a period of accelerated change that could in future be regarded as the Third Age of JavaScript.”

Shawn Wang

Electron 9.0.0 Released — The popular cross platform desktop app framework gets more dependency bumps and is now running on Chromium 83, V8 8.3, and Node.js 12.14. There’s an integrated PDF viewer now, if you need that.

GitHub Inc.

⚡️ Quick bytes:

JavaScript is 25 years old at.. roughly now!

Microsoft has been showing off its work getting React Native on macOS and some other new Windows features.

Vue has made it into the 'adopt' zone of ThoughtWorks' languages and frameworks technology radar (and Vue 3.0.0 beta 14 is out too.)

Ionic are running IoniConf 2020 online next month (June 24).

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📚 Tutorials and Opinions

▶ (Re)-Implementing The Easiest JavaScript Game Ever — Have you ever played the ‘running dinosaur’ game in Chrome when your connection goes down? This is a fun 8 minutes spent reimplementing the basic mechanic. It’s scrappy, but that’s kinda the point. If you like his style, he’s done a 2 minute video flying through the development of another arcadey game.

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The Architecture of a Serverless, Vue.js-Powered Content Management System — Not only does this outline the AWS infrastructural architecture pretty well, there’s code for you to use for your own setup if you wish.

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Understanding Lazy-Loading in Popular Frontend Frameworks — How Angular, React, and Vue handle on-demand loading of components.

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▶ One Developer's Five Most Used JavaScript 'Tricks' — If you’re more at the beginner end of the scale, you might appreciate six minutes spent here. Well presented.

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Stream Chat API & JavaScript SDK for Custom Chat Apps — Build real-time chat in less time. Rapidly ship in-app messaging with our highly reliable chat infrastructure.

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5 Differences Between Arrow and Regular Functions — A nice detailed look, with examples, of the differences between arrow and regular functions in JavaScript. I’m sure one or two of these will be new to many of you.

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Dropbox's Great CoffeeScript to TypeScript Migration of 2017 — A deep retrospective from the Dropbox team on migrating hundreds of thousands of lines of CoffeeScript to TypeScript, sharing details on why they chose TypeScript and the challenges faced. “Fast forward to 2020, we now have over two million lines of TypeScript at Dropbox.”

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Promise.all vs Promise.allSettled — “I was reading the MDN docs on JavaScript promises and realized that the difference between Promise.all and Promise.allSettled wasn’t immediately obvious.”

JonLuca DeCaro

Growing Pains: From 0 to 13,000 Dependencies — Find out how your project can go from 0 to 13,000 dependencies in just a few steps.

Nikola Đuza

Error Handling in RxJS

Eugene Ghanizadeh Khoub

A (Mostly) Complete Guide to React Rendering Behavior

Mark Erikson

How to Use Object Destructuring in JavaScript — A code-heavy tutorial looking at object destructuring, a feature introduced in ES6 that allows you to extract properties from objects and bind them to variables.

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🔧 Code & Tools

Reveal.js 4.0: An HTML Presentation Framework — A mature library takes another step forward. The homepage itself is, cleverly, a live demo (use the arrow keys). v4 adds several new features. Just want the code? Here’s the GitHub repo.

Hakim El Hattab

ac-colors: A Reactive Color Conversion and Generation Library — A lot of power under the hood here being able to convert between RGB, HSL, HEX, XYZ, LAB, and LCHab, as well as handle random color generation and contrast ratio calculation.

Vinay

MongoDB Is Easy. Now Make It Powerful. Free Download for 30 Days. — Using MongoDB Atlas? Studio 3T is the professional GUI and IDE that unlocks the power you need.

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umi-request: A Modern HTTP Request Tool Based on Fetch — An attempt at combining some of the niceties of Axios with the modernity of the Fetch API to get the best of both worlds.

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Howler.js: An Audio Library for The Modern Web — Makes things easier cross-platform. Uses the Web Audio API but can fall back to HTML5 Audio.

James Simpson

Vue Class Store: Universal Vue Stores You Write Once and Use Anywhere — We’ll let it speak for itself: “I’ll give you reactivity, computed properties and watches, written in standard JavaScript or TypeScript, with no setup or boilerplate, and you can use me anywhere.”

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New Integration: PostgreSQL Instrumented for Node.js

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Vue Formulate: The Easy Way to Build Forms with Vue.js — First linked a few months ago, this has come on leaps and bounds since with grouped fields, a way to stop validation, slots for customization, and more.

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NanoPop: A Minimalistic Positioning Engine — In a race to do things in as few bytes as possible, NanoPop aims to be much smaller than even PopperJS for positioning things like tooltips and popovers.

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#Javascript Branding #Javascript Weekly

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douchebagbrainwaves · 5 years ago

Text

I'VE BEEN PONDERING COMBINATOR

And no, you can't tell what the attitude of the aircraft is. This kind of expert witness can add credibility, even if you didn't grow it? In business there are certain situations in which certain investors like certain kinds of helplessness. Think about what it takes to start a startup, don't design your product to market early, but that you should have access to the system from anywhere. But it is a spam, which I use with an external monitor and keyboard in my office, and by definition only a minority of investors can decide in 20 minutes, surely the next round, when customers compare your actual products. Creating such a corpus would be useful to let two people edit the same document, for example. But this group must be small. It's hard to predict now, I'd say that yes, surprisingly often it can.

You have to start as a consulting firm. What are you going to recognize a good designer? 3 times in my spam corpus, the probability is. I'm often mistaken about where these bottlenecks are. This just seems to have been offered by the newer colleges, particularly American ones. Perhaps we should do what users think it will surprise people how many things are going, and have them do most of the way. You don't have to be secretive with other companies, and sales depends mostly on seniority. Plus there aren't the same forces, they still seem to have been nerds in high school it was probably understood that you were supposed to read Hugo's Les Miserables. One minute you're going to have a new idea every week will be equally fatal. Each person should just do what you would call a real job. But as of this writing the empirical evidence points that way: pretty much 100% of startups that raise money.1

Since capital is no longer needed, big companies won't be able to decrease without having to think about before: how not to die. You may need to think more about this project, I can say is that I don't think many people realize how fragile and tentative startups are in competitive businesses, you not only enjoy, but admire. Do you think Shakespeare was gritting his teeth and diligently trying to write it all yourself. So my guess is that Microsoft will develop some kind of wrongdoing. To my surprise, they said no, but they'd be dwarfed by the number of completed test drives, our revenue growth increased by 50%, just from that change.2 They hate to release something that could be weeded out.3 If it isn't, and you come home one day to be as big as a company with only three programmers. Basic, the IBM AS400, VRML, ISO 9000, the SET protocol, VMS, Novell Netware, and CORBA, among others, Tim O'Reilly, Geoff Ralston, and Garry Tan for reading drafts of this, and I noticed a remarkable pattern in them. Html, but I feel safe in predicting that whatever they have now, it probably cost us little to reject people whose characters we had doubts about even if we thought they'd be successful. Reading The Nude is like a pass/fail course. In other words, does not begin by creating a design that he then imposes on the users, instead of sitting in your grubby apartment listening to users complain about bugs in your software, but I don't see why one couldn't, by a similar process. I remember thinking Ah, so this answer works out to be surprisingly easy to compete.

If so, your old tastes were not merely different, but if the winner/borderline/hopeless progression has the sort of pork-barrel project where a town gets money from the poor, not increasing it. And what do they need to run spreadsheets on it, the best response is neither to bluff nor give up, and made up by people no different from you. You may have expected recipes for coming up with good answers.4 The fashion for the name Gary began when the actor Frank Cooper adopted the name of the artist. That has always been a stream of new startups that might otherwise not have existed. Algol isn't good enough at simulations. Nearly all companies exist to do something more serious, and that language is not obsolete is that it has made it much easier to sell to them.5 There are two kinds of fear: fear of investing in startups that get bought early and most is still unissued, and the next you're doomed. File://localhost/home/patrick/Documents/programming/python projects/UlyssesRedux/corpora/unsorted/nsearch.6

I treat mail as spam if the algorithm above gives it a probability of. Labor Board. This was certainly true in the military—that the earth moves. And that's also a sign that one is right and the other founders gets to see the old version are unlikely to complain that their thoughts have been broken by some newly introduced incompatibility. In fact, getting a normal job. In doing so you create wealth with no environmental cost. 5:29 PM subject: airbnb already spreading to pros I know you're skeptical they'll ever get. I think that's just an artifact of limitations imposed by old technology. Try to get your product to please VCs or potential acquirers. You need a certain activation energy to start a startup that avoided working on some problem, inspired by your confidence that you'll be able to get smart people to write in spoken language. It's part of the definition of property is driven mostly by people's identities.

His mind is absent from the everyday world because it's hard at work in another. In England in the 1060s, when William the Conqueror distributed the estates of the monasteries to his followers, it was like coming home.7 Angels are individual rich people who invest small amounts of their own premises, however crappy, than the startup itself, like it usually does in bad times.8 The earliest phase is usually the most productive it's ever going to extract any value from it is to get out of the big galley and put them in the news media that it became self-reinforcing nature of the web.9 They have the same problem, and possibly indeed the main cause is probably just that we have a purpose in life.10 If you're a YC startup.11 It might actually carry some weight. Outside writers tend to supply editorials of the defend-a-position variety, which make a beeline toward a rousing and foreordained conclusion. They like to get money. In fact, it may be somewhat blurry at first. How to Start a Startup March 2007 This essay is derived from Delicious/popular with voting instead of bookmarking. I often have to rephrase the question slightly.

I spend a lot of work. The latter type is sometimes called an HR acquisition. Such influence can be so specialized that this similarity is concealed, because what other people think, but they sometimes fear the wrong things. Wisdom is useful in solving problems too, and intelligence largely from cultivating them. Instead of treating them as disasters, make them easy to acknowledge and easy to fix. Even though Y Combinator is as different from what they expect of other adults. But if you make a point of packing? In fact, this is a labor of love and he wants it to be real. That seems like saying that blue is heavy, or that we'd meet them again. If you don't think things you don't want to invest in practically audition investors, and only projects that are officially sanctioned—by organizations, or parents, or wives, or at least the proximate cause may be that the behavior of algorithms for routing data through networks, for example.

The best notebooks I've found are made by a two-part one.12 In America only a few things we can say with certainty. It may look Victorian, but a fickle client or unreliable materials would not be far from the only source of economic inequality where the cause of death is listed as ran out of ideas. And whereas Wikipedia's main appeal is that it's harder for them to do? One solution here might be to design them so that the programmer could guess what library call will do what he needs. It takes time to find out is to try to do a deal in 24 hours if they need to get yourself in a situation with measurement and leverage. Duplo world of a few thousand people you'd like to like. But broadcasting isn't publishing: you're not committing to solve a problem using a network of startups than by a few, but at Viaweb bugs became almost a game. Only a tiny fraction of people who all get up in the morning. That helps would-be app stores will be too busy to shoo you away. And it's not just the mob you need to do is start one.

Notes

A day job.

But having more of the most visible index of that generation had been trained. Reporters sometimes call us VCs, I mean that if you have a different type of thinking, but it doesn't seem to be free to work with me there.

If an investor is just the most successful startups of all tend to be higher, as accurate to call the years after Lisp 1. Some would say we depend on Aristotle more than most people are like, and stir. Actually this sounds to him? An investor who's seriously interested will already be programming in college.

Only founders of failing startups would even be symbiotic, because we know nothing about the millions of dollars a year of focused work plus caring a lot of the words won't be trivial. On the other reason it used a TV as a cold email. 'Math for engineers' classes sucked mightily.

There need to go the bathroom, and made more margin loans. Perl has. Download programs to encourage more startups in Germany told me: One YC founder told me that if you have an investor derives mostly from looking for something that was the reason it used a TV as a motive, and the 4K of RAM was in a city with few other startups must have been peculiarly vulnerable—perhaps partly because companies then were more at home at the end of the taste of apples because if people can see how universally faces work by their prevalence in advertising. But it's hard to say what was happening in them to be discovered.

If you have to admit there's no lower bound to its precision. And in any era if people are trying to sell your company right now. If they were shooting themselves in the latter without also slowing the former depends a lot like intellectual bullshit.

I don't want to lead. Structurally the idea of happiness from many older societies.

Family, school, and only one founder take fundraising meetings is that promising ideas are not very discerning.

If anyone wants.

Japanese car companies have never been the first type, and—and probably harming the state of technology. Another thing I learned from this experiment is that Digg is notorious for its shares will inevitably arise.

If you ask that you're small and use whatever advantages that brings.

Not all were necessarily supplied by the size of the first scientist.

#automatically generated text #Markov chains #Paul Graham #Python #Patrick Mooney #whatever #stores #sign #definition #lot #deal #subject

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cstesttaken · 8 years ago

Text

26 Marketing Tools for Non-Tech-Savvy Marketers – SEO NYC & Digital Marketing

Marketing tools are essential for streamlining and automating the more arduous aspects of the process.

The only issue is that you’ve got to actually learn how to use them.

You have to learn their capabilities, their limitations as well as their nuances.

It’s no biggie if you’re tech-inclined.

But what if you’re not so tech-savvy?

Using marketing tools can nearly negate the purpose if it’s a struggle just to figure them out.

That’s why I compiled a list of 26 marketing tools for non-tech-savvy marketers.

Each one is practical and user-friendly and requires a minimal learning curve. Many are even free.

Content creation

Let’s start with the absolute basics: WordPress.

You could consider it to be the “OG” of content management systems.

As of late 2015, it powered 25% of the world’s websites.

And it’s very likely that number is even bigger today.

A large part of WordPress’ appeal is its utter simplicity and non-technical nature.

You can create and maintain a beautiful website with literally zero knowledge of coding.

And if you happen to understand HTML, you can completely crush it.

If you want to create a site for your business or blog, I highly recommend WordPress.

When it comes to cloud storage, I think of Google Drive as being the universal platform.

I can’t count the number of times I’ve worked with clients or business partners who’ve made Google Drive their platform of choice.

Like most Google products, it’s super intuitive and easy to use.

I use it for writing and backing up content as well as for sharing content with others.

It’s perfect if you have multiple people working on a project because sharing and editing is a cinch.

Besides docs, you can create slideshows, drawings, spreadsheets, and more.

I don’t care if you’re Mark Twain, everyone is bound to make mistakes when writing.

Whether it’s a silly spelling error or poor grammar, it’s impossible to catch everything.

But Grammarly will do just that (or pretty darn close to it).

Add it to Chrome, and Grammarly will monitor everything you write, point out any issues, and offer advice on how to resolve them.

It goes above and beyond Word and will make you look like an expert even if your writing skills are lackluster:

The cool thing is that it will also scan your emails before sending them out so you don’t look like an idiot when corresponding to customers or clients.

Word count is kind of a big deal, especially if you’re writing long-form content and need to reach a specific number of words.

But not all online writing platforms display word count.

I love this tool because I can quickly copy and paste a body of text, and Word Counter will let me know how many words I’ve written.

It’s super quick, and I’ve never experienced any sort of glitch.

Content ideas

Coming up with new ideas for content can be a major struggle.

Even if you’re an expert, it’s not always easy to come up with stellar ideas.

I’ve found Google Trends to be a great place for getting a sense of what’s popular at the moment.

Often, it will point me in the right direction, and I can then use it to gauge the exact interest in a particular topic.

For instance, here’s how the interest in content marketing has grown over the past five years:

Using Alltop is a breeze.

Simply type in a search phrase, and hundreds of popular blog posts on that topic will pop up:

Words cannot express how much I love BuzzSumo.

Pretty much anyone can figure it out within minutes, and it’s the perfect tool for generating an arsenal of content ideas.

But what separates it from other tools is the fact that it provides you with key info such as:

how much engagement content receives

who is sharing it

links pointing back to the content

The only caveat is that you must purchase the Pro version to unlock all the features.

But you can still do a basic search with the free version.

This one is a bit like the Google Keyword Tool, only simpler.

Enter a search term, and Ubersuggest will spit out dozens or even hundreds of ideas:

It’s really easy to use, and it’ll keep supplying you with topics whenever you need them.

Communication and collaboration

If WordPress is the OG CMS, Basecamp is the OG of project management and team collaboration.

Countless other products have been developed, many of which are cooler and sexier.

But Basecamp still retains its status and continues to be one of the big dogs.

I love its clean interface and how intuitive it is.

It’s very non-intimidating even for the most non-tech-savvy of marketers.

At this point, you probably know I’m big on visuals.

Images make it easier for me to absorb information and stay on top of my game.

That’s why I love Trello.

It involves a system of boards where you can communicate with colleagues and keep tabs on project progress.

It can easily be scaled up or down as necessary and can really boost productivity.

I know many people who swear by it.

This is another visual-oriented platform that I’ve used on several occasions.

I prefer Basecamp over Asana, but it’s the number one team-collaboration platform for many marketers.

In fact, some companies that use it include TED, The New Yorker, and Uber.

My favorite aspect of Asana is the ease with which I can track a project from start to finish.

I’m a stickler for deadlines, so this helps me ensure they’re always met without a lot of stress.

When I think of Slack, I think of hipsters. But in a very good way.

It’s perhaps the coolest, sleekest, sexiest collaboration app in existence.

And it’s dead simple to use.

Slack revolves around creating “channels” where you communicate with team members either publicly or privately.

Drag and drop your files to share with others, and search your archive any time you need specific information.

Task management

I stay busy, so it’s easy to feel overwhelmed when I’m bombarded with a barrage of tasks on a daily basis.

One of my favorite weapons to counter that is Wunderlist.

I place it on my desktop so I can see exactly what’s going on and what I need to take care of on any given day.

And, of course, I can also access it from my smartphone or tablet.

I can easily save links, photos, and other media I want to keep.

I also use it to set reminders of specific tasks’ deadlines and make note of any business/project ideas that pop into my head.

In other words, Wunderlist helps me keep my you-know-what together.

If you use WordPress (like I recommend), you’ll want to take advantage of this plugin.

It’s a little like Google Calendar, but specifically for scheduling your blog posts.

Manage drafts

See what’s been posted

See what needs to be posted

Manage posts from different authors

Like most things on WordPress, it’s user-friendly, and it doesn’t take a rocket scientist to figure out its features.

The tagline of this platform is “Accomplish more, every day.”

And that’s fitting because I’ve found Todoist to be a major catalyst for productivity.

You simply record tasks, prioritize them as needed, collaborate with others, and get stuff done.

I love its no-nonsense interface and minimalist vibe.

SEO

This is another WordPress plugin and one that I highly recommend if you’re fairly new to the SEO game.

Here’s a screenshot of its features:

In other words, it handles nearly every major aspect of SEO.

The best part is its simplicity.

I love Yoast SEO because it’s very hands off and automates many of the more arduous SEO tasks like creating optimized URLs, keeping track of keyword density, and so on.

Before you publish your content, Yoast SEO will rate its readability and your keyword usage by giving it a color: red for poor, orange for okay, and green for good.

If you loathe the technical nature of SEO, this is a great plugin to use.

If you were to use only one tool for performing keyword research, this is it.

Even the biggest SEO nerd will agree that it’s useful because you’re gathering data right from the horse’s mouth—Google itself.

The cool thing is that you don’t need to be technically adept to figure it out. Most of the features are pretty self-explanatory.

In my opinion, Moz is perhaps the Internet’s number one resource for all things SEO.

I especially love its Whiteboard Fridays, offering in-depth analysis and insight.

If you’re looking for a quick and easy way to determine key SEO metrics like links, page authority, and domain authority, I highly recommend MozBar.

Simply add it to your Chrome toolbar, and you’re good to go.

This is another great SEO tool that’s amazingly easy to use.

Just enter a URL or keyword, and you instantly get a boatload of useful information such as:

Organic search volume

Backlinks

Top organic keywords

Main organic competitors

Branded search

If you’re looking to perform competitive analysis for keyword or content opportunities, look no further than SEMrush.

Images

If you’re creating content, you’ll need plenty of beautiful visuals.

In my opinion, Canva is hands down one of the best platforms for creating your own images and documents from scratch.

It’s really easy, and Canva offers a wide array of images that are totally free.

You can modify them as needed for your content or for branding purposes.

The best part is that you can do this with virtually no design experience.

PicMonkey is a photo editor that allows you to design, resize, do touch-ups, create collages, and a lot more.

Using it is no sweat even if you have no clue what you’re doing in terms of design.

It’s perfect if you have your own images you want to customize, and PicMonkey helps you make them look like a million bucks.

Here’s my take on stock photos.

I prefer to pay for them and get the best of the best.

But if you’re just starting out or are on a budget, Pixabay is one of my top picks.

Everything is royalty-free and available for the public to download, modify, and distribute.

They have a massive archive of pictures that covers most topics, and the quality of their images has really improved over the past couple of years.

Here are just a few samples:

Creative Commons is basically an aggregator of images free to use for commercial purposes. These images can be modified, adapted, or built upon.

You enter a search query, and choose from multiple platforms like Flickr, Wikimedia Commons, Open Clip Art Library, and even Google.

It’s a great tool for streamlining your image search.

Metrics

There are countless metrics platforms out there for measuring your website’s performance, traffic numbers, and so on.

But I think it’s safe to say that Google Analytics is the be-all and end-all tool.

The free version is more than sufficient for diagnosing your website and, in my opinion, quite easy to use.

I’ll admit there is a bit of a learning curve, but most people can figure out the basics in a day or two.

Bitly is perhaps best known for being a URL shortener.

In fact, I use it all the time for condensing URLs on my Twitter page:

But it’s useful for way more than that.

Bitly allows you to track individual links and gather key information about their performance.

You can tell what your audience is responding to (or not) and tweak your marketing efforts accordingly.

Despite its comprehensiveness and level of detail, I consider it to be one of the most user-friendly analytics tools.

You can see what’s happening on your website in real time, monitor the actions of visitors, and even look at heat maps, which I love.

I know some marketers who actually choose Clicky over Google Analytics.

Conclusion

I totally understand the frustration that many non-tech-savvy marketers feel.

There are many tools that are great but require serious knowledge to be utilized properly.

These can really cramp your style and drive you crazy.

But the marketing tools I’ve listed are ones that will get the job done without being overly complex.

With most, the core features can be learned within just a few minutes.

This way, you can spend less time trying to figure out your marketing tools and more time reaching your audience.

Can you suggest any other easy-to-use marketing tools?

Source

https://seonycdigitalmarketing.wordpress.com/2017/03/24/26-marketing-tools-for-non-tech-savvy-marketers/

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anseladams03 · 8 years ago

Text

26 Marketing Tools for Non-Tech-Savvy Marketers

Marketing tools are essential for streamlining and automating the more arduous aspects of the process.

The only issue is that you’ve got to actually learn how to use them.

You have to learn their capabilities, their limitations as well as their nuances.

It’s no biggie if you’re tech-inclined.

But what if you’re not so tech-savvy?

Using marketing tools can nearly negate the purpose if it’s a struggle just to figure them out.

That’s why I compiled a list of 26 marketing tools for non-tech-savvy marketers.

Each one is practical and user-friendly and requires a minimal learning curve. Many are even free.

Content creation

1. WordPress

Let’s start with the absolute basics: WordPress.

You could consider it to be the “OG” of content management systems.

As of late 2015, it powered 25% of the world’s websites.

And it’s very likely that number is even bigger today.

A large part of WordPress’ appeal is its utter simplicity and non-technical nature.

You can create and maintain a beautiful website with literally zero knowledge of coding.

And if you happen to understand HTML, you can completely crush it.

If you want to create a site for your business or blog, I highly recommend WordPress.

You can learn how to do it from scratch with this video from Quick Sprout.

2. Google Drive

When it comes to cloud storage, I think of Google Drive as being the universal platform.

I can’t count the number of times I’ve worked with clients or business partners who’ve made Google Drive their platform of choice.

Like most Google products, it’s super intuitive and easy to use.

I use it for writing and backing up content as well as for sharing content with others.

It’s perfect if you have multiple people working on a project because sharing and editing is a cinch.

Besides docs, you can create slideshows, drawings, spreadsheets, and more.

3. Grammarly

I don’t care if you’re Mark Twain, everyone is bound to make mistakes when writing.

Whether it’s a silly spelling error or poor grammar, it’s impossible to catch everything.

But Grammarly will do just that (or pretty darn close to it).

Add it to Chrome, and Grammarly will monitor everything you write, point out any issues, and offer advice on how to resolve them.

It goes above and beyond Word and will make you look like an expert even if your writing skills are lackluster:

The cool thing is that it will also scan your emails before sending them out so you don’t look like an idiot when corresponding to customers or clients.

I highly recommend it!

4. Word Counter

Word count is kind of a big deal, especially if you’re writing long-form content and need to reach a specific number of words.

But not all online writing platforms display word count.

I love this tool because I can quickly copy and paste a body of text, and Word Counter will let me know how many words I’ve written.

It’s super quick, and I’ve never experienced any sort of glitch.

Content ideas

5. Google Trends

Coming up with new ideas for content can be a major struggle.

Even if you’re an expert, it’s not always easy to come up with stellar ideas.

I’ve found Google Trends to be a great place for getting a sense of what’s popular at the moment.

Often, it will point me in the right direction, and I can then use it to gauge the exact interest in a particular topic.

For instance, here’s how the interest in content marketing has grown over the past five years:

6. Alltop

Using Alltop is a breeze.

Simply type in a search phrase, and hundreds of popular blog posts on that topic will pop up:

I use this for brainstorming all the time, and Alltop has helped me come up with some epic ideas for blog posts.

7. BuzzSumo

Words cannot express how much I love BuzzSumo.

Pretty much anyone can figure it out within minutes, and it’s the perfect tool for generating an arsenal of content ideas.

But what separates it from other tools is the fact that it provides you with key info such as:

how much engagement content receives

who is sharing it

links pointing back to the content

The only caveat is that you must purchase the Pro version to unlock all the features.

But you can still do a basic search with the free version.

8. Ubersuggest

This one is a bit like the Google Keyword Tool, only simpler.

Enter a search term, and Ubersuggest will spit out dozens or even hundreds of ideas:

It’s really easy to use, and it’ll keep supplying you with topics whenever you need them.

Communication and collaboration

9. Basecamp

If WordPress is the OG CMS, Basecamp is the OG of project management and team collaboration.

Countless other products have been developed, many of which are cooler and sexier.

But Basecamp still retains its status and continues to be one of the big dogs.

I love its clean interface and how intuitive it is.

It’s very non-intimidating even for the most non-tech-savvy of marketers.

10. Trello

At this point, you probably know I’m big on visuals.

Images make it easier for me to absorb information and stay on top of my game.

That’s why I love Trello.

It involves a system of boards where you can communicate with colleagues and keep tabs on project progress.

It can easily be scaled up or down as necessary and can really boost productivity.

I know many people who swear by it.

11. Asana

This is another visual-oriented platform that I’ve used on several occasions.

I prefer Basecamp over Asana, but it’s the number one team-collaboration platform for many marketers.

In fact, some companies that use it include TED, The New Yorker, and Uber.

My favorite aspect of Asana is the ease with which I can track a project from start to finish.

I’m a stickler for deadlines, so this helps me ensure they’re always met without a lot of stress.

12. Slack

When I think of Slack, I think of hipsters. But in a very good way.

It’s perhaps the coolest, sleekest, sexiest collaboration app in existence.

And it’s dead simple to use.

Slack revolves around creating “channels” where you communicate with team members either publicly or privately.

Drag and drop your files to share with others, and search your archive any time you need specific information.

Slack makes it easy.

Task management

13. Wunderlist

I stay busy, so it’s easy to feel overwhelmed when I’m bombarded with a barrage of tasks on a daily basis.

One of my favorite weapons to counter that is Wunderlist.

I place it on my desktop so I can see exactly what’s going on and what I need to take care of on any given day.

And, of course, I can also access it from my smartphone or tablet.

I can easily save links, photos, and other media I want to keep.

I also use it to set reminders of specific tasks’ deadlines and make note of any business/project ideas that pop into my head.

In other words, Wunderlist helps me keep my you-know-what together.

14. WordPress Editorial Calendar Plugin

If you use WordPress (like I recommend), you’ll want to take advantage of this plugin.

It’s a little like Google Calendar, but specifically for scheduling your blog posts.

You can:

Manage drafts

See what’s been posted

See what needs to be posted

Manage posts from different authors

Like most things on WordPress, it’s user-friendly, and it doesn’t take a rocket scientist to figure out its features.

15. Todoist

The tagline of this platform is “Accomplish more, every day.”

And that’s fitting because I’ve found Todoist to be a major catalyst for productivity.

You simply record tasks, prioritize them as needed, collaborate with others, and get stuff done.

I love its no-nonsense interface and minimalist vibe.

SEO

16. Yoast SEO

This is another WordPress plugin and one that I highly recommend if you’re fairly new to the SEO game.

Here’s a screenshot of its features:

In other words, it handles nearly every major aspect of SEO.

The best part is its simplicity.

I love Yoast SEO because it’s very hands off and automates many of the more arduous SEO tasks like creating optimized URLs, keeping track of keyword density, and so on.

Before you publish your content, Yoast SEO will rate its readability and your keyword usage by giving it a color: red for poor, orange for okay, and green for good.

If you loathe the technical nature of SEO, this is a great plugin to use.

17. Google Keyword Planner

If you were to use only one tool for performing keyword research, this is it.

Even the biggest SEO nerd will agree that it’s useful because you’re gathering data right from the horse’s mouth—Google itself.

The cool thing is that you don’t need to be technically adept to figure it out. Most of the features are pretty self-explanatory.

18. MozBar

In my opinion, Moz is perhaps the Internet’s number one resource for all things SEO.

I especially love its Whiteboard Fridays, offering in-depth analysis and insight.

If you’re looking for a quick and easy way to determine key SEO metrics like links, page authority, and domain authority, I highly recommend MozBar.

Simply add it to your Chrome toolbar, and you’re good to go.

19. SEMrush

This is another great SEO tool that’s amazingly easy to use.

Just enter a URL or keyword, and you instantly get a boatload of useful information such as:

Organic search volume

Backlinks

Top organic keywords

Main organic competitors

Branded search

If you’re looking to perform competitive analysis for keyword or content opportunities, look no further than SEMrush.

Images

20. Canva

If you’re creating content, you’ll need plenty of beautiful visuals.

In my opinion, Canva is hands down one of the best platforms for creating your own images and documents from scratch.

It’s really easy, and Canva offers a wide array of images that are totally free.

You can modify them as needed for your content or for branding purposes.

The best part is that you can do this with virtually no design experience.

21. PicMonkey

PicMonkey is a photo editor that allows you to design, resize, do touch-ups, create collages, and a lot more.

Using it is no sweat even if you have no clue what you’re doing in terms of design.

It’s perfect if you have your own images you want to customize, and PicMonkey helps you make them look like a million bucks.

22. Pixabay

Here’s my take on stock photos.

I prefer to pay for them and get the best of the best.

But if you’re just starting out or are on a budget, Pixabay is one of my top picks.

Everything is royalty-free and available for the public to download, modify, and distribute.

They have a massive archive of pictures that covers most topics, and the quality of their images has really improved over the past couple of years.

Here are just a few samples:

23. Creative Commons

Creative Commons is basically an aggregator of images free to use for commercial purposes. These images can be modified, adapted, or built upon.

You enter a search query, and choose from multiple platforms like Flickr, Wikimedia Commons, Open Clip Art Library, and even Google.

It’s a great tool for streamlining your image search.

Metrics

24. Google Analytics

There are countless metrics platforms out there for measuring your website’s performance, traffic numbers, and so on.

But I think it’s safe to say that Google Analytics is the be-all and end-all tool.

The free version is more than sufficient for diagnosing your website and, in my opinion, quite easy to use.

I’ll admit there is a bit of a learning curve, but most people can figure out the basics in a day or two.

25. Bitly

Bitly is perhaps best known for being a URL shortener.

In fact, I use it all the time for condensing URLs on my Twitter page:

But it’s useful for way more than that.

Here’s the deal.

Bitly allows you to track individual links and gather key information about their performance.

You can tell what your audience is responding to (or not) and tweak your marketing efforts accordingly.

26. Clicky

Finally, there’s Clicky.

Despite its comprehensiveness and level of detail, I consider it to be one of the most user-friendly analytics tools.

You can see what’s happening on your website in real time, monitor the actions of visitors, and even look at heat maps, which I love.

I know some marketers who actually choose Clicky over Google Analytics.

Conclusion

I totally understand the frustration that many non-tech-savvy marketers feel.

There are many tools that are great but require serious knowledge to be utilized properly.

These can really cramp your style and drive you crazy.

But the marketing tools I’ve listed are ones that will get the job done without being overly complex.

With most, the core features can be learned within just a few minutes.

This way, you can spend less time trying to figure out your marketing tools and more time reaching your audience.

Can you suggest any other easy-to-use marketing tools?

from Quick Sprout http://ift.tt/2nZ2PCK from Blogger http://ift.tt/2o0bjJP March 24, 2017 at 10:39PM

#Quick Sprout

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douchebagbrainwaves · 6 years ago

Text

WHY I'M SMARTER THAN TIME

I was in Africa last year and saw a lot of them about halfway to Lisp. No big company can do much better than that. How long do you think it would take them on average to realize that it would ruin the product they hoped to be laughing all the way up into conscious knowledge. One developer told me: As a result of their process, the App Store, and it's a bad sign if they weren't; it would mean you were being too easy on them. The reason they were funding all those laughable startups during the late 90s was that they hoped to sell them to gullible retail investors; they hoped to be laughing all the way to go. What would make the painting more interesting to people? If you want to reach users, you do not talk about Fight Club. And companies offering Web-based software, because writing desktop software, because writing applications for them seemed an attainable goal to larval startups. A round. I realized that it reflects reality: software development is affected by the way; it's the main difference between children and adults. System administrators can become cranky and unresponsive because they're not directly exposed to competitive pressure: a salesman has to deal with competitors' software, but for those who make it often try to trick us.1 This argument applies to every aspect of server administration: not just security, but uptime, bandwidth, load management, backups, etc.2

I'm not saying that no one will care how comfortingly orthodox your technology choices were. But in fact if you narrow the definition of beauty to something that works a certain way on humans, and perhaps others that would appeal equally to your friends, to people in Nepal, for a time as the manager of a nightclub in Miami. The statements that make people maddest are those they worry might be believed. I see patterns in my programs, I consider it a sign of something you need to fix anything? This is not just something to worry about bugs, and you've even set up mechanisms to compensate for it e. Computers are in this phase now. Suppose for the sake of simplicity that this is a naive and outdated ambition. That's the best-looking spreadsheet using HTML, but you don't have to rely on benchmarks, for example—that's not an innovation, in the sense of investors who take the lead in the old sense of managing the round. O'Reilly was wearing a suit, a sight so alien I couldn't parse it at first. You can figure out the rest as you go.

For server-based applications offer a straightforward way to outwork your competitors. The application that pushed desktop computers out into the mainstream was VisiCalc, the first spreadsheet. VisiCalc was such an advance, in its time, that people bought Apple IIs just to run it. It will always suck to work for a long time. So working in a group of your peers? 0 meaning the web as it was meant to be used.3 It's not considered improper to make disparaging remarks about Americans, or the English.

But the real problem for Microsoft, and I think this is the main cause of what Jonathan Erickson calls the programming language renaissance.4 If you're writing software that has to run on Suns than on Intel boxes: a company that uses Suns is not interested in saving money and can safely be charged more. What we mean by a programming language, at least, can avoid believing them.5 One of the many things we do at Y Combinator is teach hackers about the inevitability of schleps. The statements that make people mad are the ones they worry might be true. In a lot of popular sites were loaded with obtrusive branding that made them slow to load and sent the user the message: this is our site, not yours.6 I would think of some ideas that would be just as great an achievement as the ceiling of the Sistine Chapel is more interesting to people? And technology for targeting ads continues to improve. If you can discuss a document with your colleagues, why can't you edit it?7 0 first arose in a brainstorming session between O'Reilly and Medialive International. The argument against this approach usually hinges on security: if access is easier for employees, it will take three times as long in another language, it will be the same or even better.

Its purpose is to shield the pointy-haired boss, the difficulty of hiring programmers, I think, is that evil begets stupidity. Particularly lions. How do you do with a gleeful laugh.8 And when you can do about this conundrum, so the best plan is to go for the smaller customers first.9 In any period, it should be easy to figure out which of our taboos they'd laugh at. One developer told me: As a result of their process, the App Store, and it's combined with the emotion Really, it's Apple's fault. Advanced users are more forgiving about bugs, and then try to pry apart the cracks and see what's underneath. The version on the App Store.

You'd have to get iPhones out of programmers' hands. It's a lot easier for a couple of 20 year old hackers who are too naive to be intimidated by the idea.10 You may feel lousy an hour after eating that pizza, but eating the first couple bites feels great. It may also be because if you don't, no one ever called us on it. For example, nearly all the food around you would be bad for you. 0 first arose in a brainstorming session between O'Reilly and Medialive International. At Viaweb, software included fairly big applications that users talked to directly, programs that pretended to be users to measure performance or expose bugs, programs for diagnosing network troubles, programs for doing backups, interfaces to outside services, software that drove an impressive collection of dials displaying real-time server statistics a hit with visitors, but indispensable for us too, modifications including bug fixes to open-source software, and a great many configuration files and settings. Viaweb.11 Never send them email unless they explicitly ask for it. And software that's released in a series A round, before the VCs invest they make the company set aside a block of stock for future hires—usually between 10 and 30% of the company.12 There was that same odd atmosphere created by a large number of people you interact with is about right.

Or rather, investors who do that will get easier too. You may not believe it, but I haven't seen it. But your goal here wasn't to provide a service estimating people's ability. Nearly all the judgements made on children are of this type, so we started giving version numbers to our software too.13 The trick I recommend is to take yourself out of the bust, there would need to be software for making them, so we get into the habit early in life of thinking that all judgements are. You see this less with Windows, because hackers don't use it. 0 in the name of the Web 2. Humans have a lot in common, you'd also find they agreed on a lot.

Notes

Which means the investment community will tend to become one of the proposal.

While environmental costs should be especially conservative in this new world. I mean no more unlikely than it was the reason there have historically been so many of the young care so much on luck. The chief lit a cigarette.

He did eventually graduate at about 26.

In the Valley.

The continuing popularity of religion is the true kind.

You can build things for programmers, it will thereby expose it to colleagues. The question to ask about what was happening on Dallas, and b made brand the dominant factor in high school is that it is to how Henry Ford got started as a game, Spacewar, in the general manager of the canonical could you build this? Nor do we push founders to have too few customers even if they knew.

This is what we now call science. Com in order to pick the words we use for good and bad measurers. Two Hundred Years. What I dislike is editing done after the Physics in the same superior education but had a demonstration of the startup.

The trustafarians' ancestors didn't get rich by creating wealth—university students, heirs, rather technical sense of getting too high a valuation. I'd argue the long term than one who passes. Gauss was supposedly asked this when comparing techniques for stopping spam.

Siegel points out that another way to create one of those you should probably question anything you believed as a first approximation, it's implicit that this filter runs on. I'd almost say to the usual way will prove to us. Instead of earning the right sort of Gresham's Law of conversations.

There is usually slow growth or excessive spending rather than lose a prized employee. According to a super-angels hate to match. As Secretary of State and the manager, which is all about to give up your anti-dilution provisions, even the flaws of big companies can even be worth doing something different if it was 94% 33 of 35 companies that seem excusable according to certain somewhat depressing rules many of the mail by Anton van Straaten on semantic compression.

So instead of hiring them. So if anything they could attribute to the same. If spammers get good enough at obscuring tokens for this. Don't be fooled by grammar.

VCs if the president faced unscripted questions by giving a press hit, but investors can get very emotional. So it's a proxy for revenue growth, it's probably a cause them to. You need to get out of their peers.

Ideas are one of the word wisdom in ancient Egypt took exams, but art is brand, and you start to be like a core going critical. The nationalistic idea is bad. Some are merely ugly ducklings in the back of your mind what's the right sort of person who would have become good friends.

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