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SQL Hegemony: A Sad State of Affairs

It appears that there are people who don't recognize SQL as a tradeoff.

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It appears that there are people who don't recognize SQL as a tradeoff.

Here's a complex two-part question that can only come from folks who firmly believe in the magic of SQL.

The sentence that got my attention was "Python has basically made SQL obsolete as a language for data structure manipulation". My question would be about scaling.  If [we? you?] have 30 million rows in a table, would Python still be better than straight up SQL? The other question would be about the amount of time to come up to speed. It just seems easier to learn SQL than Python.

Also, in working with legacy DBA's who are starting to learn Cassandra, I see similar magical thinking. Somehow, Oracle's behavior can become a baseline in some people's minds. When Cassandra's column database shows different behavior, there are DBA's who are surprisingly quick to portray Cassandra as "wrong" or "confusing." Worse, they'll waste a lot of time insisting that Cassandra is misusing the term "key" because Cassandra's idempotency policy means multiple INSERTS with the same primary key are handled differently from Oracle. Labeling Cassandra as "wrong" is a similar problem to the question.

Let's unpack the "SQL is better" question and see why this seems so sad.

I'm not going to address the quote ("Python has basically made SQL obsolete...") since that wasn't part of the question. That's just background. And everyone seems to agree on this. The question appears to be related to clinging to SQL in spite of Python's advantages.

But first, I have to note that the question violates some pretty serious rules of engagement.

The Rules for Questions

Asking hand-waving hypotheticals is generally a pretty bad practice. Sometimes, I'm completely intolerant, and refuse to engage. In this case, I felt compelled to respond, in spite of the vacuity of the question. 

First, of course, "better" is undefined in the question. That essentially ends any conversation.

Second, there's no code. It's very hard to discuss anything without code. All the hand-waving is essentially meaningless because when code finally does show up, it will fit into some edge or corner not properly covered by hand-waving.

Third, there's no possibility of code. There's nothing resembling a tangible use case or scenario that can be turned into code for comparison purposes.

Also,  the question seems to be creating a false dichotomy between SQL and Python. This is a more subtle issue, and we'll look at this, too.

Python Better Than SQL

We can assign a number of potential meanings to "better". Some other phrases -- "30 million rows in a table" and "about scaling" -- could be dismissed as mere noise. Perhaps they're hints.

Let's assume it's about size of storage. Can Python deal with 30 million rows of data? Since we don't know the row size, there is no actual answer. Without transactions or activities of some kind, we're similarly bereft of the kinds of details that lead to a sensible answer.

Let's say we're limited to 32Gb of memory. If the row size is up to 1Kb, we can fit all of the data in memory. We're pretty much done with size and speed.  Python wins for the canonical CRUD operations.

Python wins because any code we write will be completely customized for the data we're given. We're freed from generalized SQL type conversion complexity, ODBC driver folderol, storage management overheads, SQL language parsing work. Just the data manipulation. No lock escalation or read consistency consideration. Done.

But wait. Not so fast, what about loading 32Gb into memory?

What about it? The problem is so delightfully vague that we have no clue what "loading" might mean. Oracle takes a while to mount a database and do other startup things. Python can open a file and slurp in the data pretty quickly. If you want to amortize the loading time, you can have smarter loader that brings in data incrementally.

def load(data, key_col):
    with data.open() as source:
        rdr = csv.reader(source)
        table = { row[key_col]: row for row in rdr }
    return table

def CRUD(table, key_col, update_col):
    row = tuple(random_text() for i in range(10))

    # INSERT INTO table(col,col,...) VALUES(val,val,...)
    table[row[key_col]]= row

    # SELECT * FROM TABLE WHERE key_col = value
    found = table[row[key_col]]
    #print( found )

    # UPDATE TABLE SET update_col = "value" WHERE key_col = value
    table[row[key_col]][update_col] = "special text"

    # DELETE FROM TABLE WHERE key_col = value
    del table[row[key_col]]

    # Is it gone?
    assert row[key_col] not in table

Rather than go for 30 million rows on this little laptop (with only 8Gb RAM), we'll load 30,000 rows each of which is about 150 characters. Small. The point, however, is this:

load 0.133, CRUD 0.176

We can load 30,000 rows of data in 133 ms.  We can do 1,000 sets of CRUD operations in 176 ms. The load time scales with total number of bytes, row size × number of rows. The CRUD operation time will barely move no matter how many rows or how big the rows are.

The problem with this kind of benchmark is that it plays to SQL's strengths. It makes SQL look like the benchmark. We're forced to show how some non-SQL language can also do what SQL does. And that's silly.

What About Bigger?

Let's pretend the number was supposed to be 30 billion rows of data. Something that clearly can't fit into memory. Wait. Traditional SQL databases struggle with this, too. Let's press on. 30 billion rows of data. Each row is at least 1K in size. 3Tb of storage. Can Python do this?

Recall that the question gives us no help in reasoning about "better".

What's the representation? 3Tb has got to be a implemented as collection of smaller files. All of the files must have a common format. Let's posit CSV. We don't really want all of this storage on a single server. We want to farm this out to several hosts. And we probably want to layer in some redundancy in case one of those hosts fails.

Okay. It might not be obvious, but we're describing the HDFS from Hadoop. We could -- without too much trouble -- implement an HDFS surrogate that has very limited functionality in Python. We can use SFTP to smear two copies of each file among a fixed-size farm of servers. Very hard-wired, unlike Hadoop.

Then the reading part of our imagined app will scroll through the collection of CSV-formatted files on each processor. We'd have to implement a Hadoop map-reduce in Python. Again. Not very difficult if we eliminate some features and stick to a very basic version map-reduce. We can coordinate the reductions by implementing a simple REST-based master-reducer that accepts the reductions from the other processors and does the final reduce.

Now we have a lot of Python language overheads. Have we failed at "better" because we polluted the solution with a fake Hadoop?


The SQL folks had to install, configure, and manage a SQL database that handled 3Tb of storage. The Python folks installed Python. Installed their fake Hadoop. Then they used a few clever abstractions to write delightfully simple map and reduce functions. Python still handles the extremely large amount of data faster than SQL. Also, it does this without some RDBMS features.

Which leads us to the second part of the question. Expressivity.

Easier to Learn

From the Question: "It just seems easier to learn SQL than Python".

This is pretty much meaningless noise. Less meaningful than the rest of the question. Having taught both, I'm confident in saying that SQL can be pretty confusing.


More importantly.

There's no rational basis for comparison.

SQL DML is a very tiny language with only a few concepts. It's not a Turing-complete programming language.

What's important is this:

We have to embed SQL in another language.

You can't actually DO anything in SQL by itself. You need another language.

In the old days, we actually wrote SQL in the middle of some other programming language source. A pre-processor replaced SQL with the other language's code. Now we use ODBC/JDBC or other drivers to execute SQL from within another language. The embedding isn't quite so literal as it once was. But it's still embedding.

The SQL vs. Programming Language is not an "either-or" situation. We never have a stark choice between SQL or "some other language." We always have to learn "some other language." Always.

That "other language" might be PL/SQL or TSQL or whatever scripting tool of choice comes bundled with the database. It isn't SQL, it's another Turing-complete language that shares SQL syntax.

Since "some other language" is required, the real question is "is there value in also learning SQL?" Or -- most importantly -- "What's the value in spreading the knowledge representation around among multiple languages?"

In some contexts, SQL can act as a lingua franca, allowing a kind of uniform access to data irrespective of the application programming language.

In most contexts, however, the SQL -- in isolation -- is incomplete. There is application processing that has semantic significance. The "do everything in stored procedures" crowd spend too much time in raging denial that application logic is still used to wrap their stored procedures.  No matter how enthusiastic one embraces stored procedures, application code still exists, and still implements semantically significant operations.

SQL is merely a short-hand notation for potentially complex algorithms. It's an optimization. SQL elects for universality via abstraction. It can't cover efficiency or scalability. We have to bind in a representation and access algorithm to compare SQL performance with another language's performance. Or scalability.

By itself, SQL is useless. So there's a false dichotomy implied by the question.

The Head-To-Head Problem

Above, I provided code that demonstrates SQL CRUD operations in Python. This is, of course, silly. It presumes that SQL is the benchmark standard which Python must meet.

What if we lift up Python as the benchmark that SQL has to meet?


We can trivially write things in Python which cannot be expressed in SQL at all.  E.g., Compute the 1000th Fibonacci Number. For fun, go to https://projecteuler.net/archivesand pick any problem and try to solve it in SQL. Try to even frame the problem in a way that the solution can be expressed in SQL. SQL has profound limitations.

Okay. That's sort of like cheating.

Let's not raise the bar quite so high, then. Here's today's problem.

I got a spreadsheet with 100's of rows of student evaluations. It may have come from Survey Monkey. Or not. It doesn't matter.

Most of the columns are some kind of Agree-Disagree scale. Other columns are comments or usernames, or stuff in an open-ended domain.

Note that I don't know which columns. And I don't care. And I don't need to care.

Here's how we tackle this in Python. It can be done in SQL. That's the point. It's not impossible. It's just kind of complex. Especially because the data loading either requires converting the data to a sequence of INSERT statements or we have to use a "loader" which lives outside the SQL language.

from collections import Counter
def summarize(data):
    with data.open() as source:
        rdr = csv.DictReader(source)
        summaries = {name: Counter() for name in rdr.fieldnames}
        for row in rdr:
            for key, value in row.items():
                summaries[key][value] += 1
    for key in sorted(summaries):
        summary= summaries[key]
        if len(summary) == 5:
            print(key, summary)
            print(key, "More than 5 values")

This is the kind of thing that people do in Python that demonstrates the limitations of SQL.  We've summarized all columns doing a count/group-by in one pass through the data. We've build Counter objects for each column name in the file. Each Counter object will collect a complete histogram for a given column. We'll do all of the columns at once.

This is scalable to millions or billions of rows and runs delightfully quickly. Doing something similar with SELECT COUNT(*) FROM TABLE GROUP BY SOMETHING is remarkably slow.  Databases are forced to do a lot of on-disk sorting and temporary file creation. The Python Counter lives in memory and works at in-memory speeds. Even for millions of rows of data.


Please define "better". Be explicit on what your goals are: speed, ACID, reliability, whatever.

Please provide code. Or provide use cases that map directly to code.

Please stop clinging to SQL. Be realistic.

Please consider the basics: Does it capture knowledge effectively? Is it expressive?

Please don't create dichotomies where none exist.

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Published at DZone with permission of Steven Lott, DZone MVB. See the original article here.

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