I maintain a couple of MySQL based applications. I don't really use any features outside of "standard SQL" is there a reason to switch over to Pg? I haven't used Pg before and usually default to MySQL.
begin;
alter table foos add answer int not null default 42;
alter table foos drop column plumbus;
update foos set name = upper(name);
create table bars (t serial);
drop table dingbats;
rollback; // Or, of course, commit
This is not possible in MySQL, or almost any other database [1], including Oracle — DDL statements aren't usually transactional. (In MySQL, I believe a DDL statement implicits commits the current transactions without warning, but I could be wrong.)
Beyond that, I'd mention: PostGIS, arrays, functional indexes, and window functions. You may not use these things today, but once you discover them, you're bound to.
[1] https://wiki.postgresql.org/wiki/Transactional_DDL_in_Postgr...
I don't know if it accomplishes anything truly new (other than ideas that aren't very useful in practice like being able to have multiple test runs going in parallel), but it's a pretty neat way to be able to do it and works well.
Lastly, if a test fails you'd typically like to leave the data behind so that you can inspect it. A transactional test that rolls back on failure won't allow that.
Without this built-in feature, I'd have used filesystem snapshots, if I didn't mind the time it'd take to stop and start Pg.
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1: https://www.postgresql.org/docs/current/manage-ag-templatedb...
Might want to mention the downside of using MySQL as well. (Am also interested to know as a daily MySQL user.)
- JSON column (actually MySQL 5.6 supports it but I doubt if it's as good as Postgres)
- Window functions (available in MySQL 8x only, while this has been available since Postgres 9x)
- Materialized views, views that is physical like a table, can be used to store aggregated, pre-calculated data like sum, count...
- Indexing on function expression
- Better query plan explanation
For indexing on function expressions in particular, the workaround we use is to add a generated column and index that.
https://www.depesz.com/2019/02/19/waiting-for-postgresql-12-...
What bothers me more, that a CTE prevents parallel execution, but I think that too is fixed with Postgres 12
MySQL 5.7 fully supports this. See https://dev.mysql.com/doc/refman/5.7/en/create-table-generat... and https://dev.mysql.com/doc/refman/5.7/en/create-table-seconda...
> JSON column (actually MySQL 5.6 supports it but I doubt if it's as good as Postgres)
Actually MySQL 5.6 doesn't support this, but 5.7 does, quite well: https://dev.mysql.com/doc/refman/5.7/en/json.html
Additionally, an ALTER TABLE blocks access to the table. Indexes can be created concurrently while other transactions can still read and write the table.
But MySQL doesn't support indexing the complete JSON value for arbitrary queries. You can only index specific expressions by creating a computed column with that expression and indexing that.
Yes and no. Generated columns in MySQL can optionally be "virtual". An indexed virtual column is functionally identical to an index on an expression.
> Additionally, an ALTER TABLE blocks access to the table.
It depends substantially on the specific ALTER and version of MySQL. Many ALTERs do not block access to the table in modern MySQL; some are even instantaneous.
> But MySQL doesn't support indexing the complete JSON value for arbitrary queries. You can only index specific expressions by creating a computed column with that expression and indexing that.
What's the difference, functionally speaking? (Asking honestly, not being snarky -- I may not understand what you are saying / what the equivalent postgres feature is?)
You create a single index, e.g:
create index on the_table using gin(jsonb_column);
And that will support many different types of conditions,
e.g.: check if a specific key/value combination is contained in the JSON:
where jsonb_column @> '{"key": "value"}'
this also works with nested values:
where jsonb_column @> '{"key1" : {"key2": {"key3": 42}}}'
Or you can check if an array below a key contains one or multiple values:
where jsonb_column @> '{"tags": ["one"]}' or where jsonb_column @> '{"tags": ["one", "two"]}'
Or you can check if all keys from a list of keys are present:
where jsonb_column ?& array['key1', 'key2']
All those conditions are covered by just one index.
Out of curiosity, how commonly is this used at scale? I'd imagine there are significant trade-offs with write amplification, meaning it would consume a lot of space and make writes slow. (vs making indexes on specific expressions, I mean. That said, you're right -- there are definitely use-cases where making indexes on specific expressions isn't practical or is too inflexible.)
It's an excruciating process though.
Meanwhile, for high-volume OLTP workloads, MySQL (with either InnoDB or MyRocks on the storage engine side) has some compelling advantages... this is one reason why social networks lean towards MySQL for their OLTP product data, and/or have stayed on MySQL despite having the resources to switch.
As with all things in computer science, there are trade-offs and it all depends on your workload :)
I really get that MySQL is good for what it does, from an engineer's point of view. It is an absolute piss-poor excuse for a database, prior to v8.0.
So what's wrong with MySQL (again, prior to v8.0, but no one seems to use the damn current version)
-Not ANSI SQL compliant (unlike Postgres)
-No CTEs/WITH clause (?!)
-no WINDOW FUNCTIONS (?!?!?!?)
-"schemas are called databases" which makes for bizarre interpretation of `information_schema` queries, which behave the same across all other DBs except mySQL. What I mean to say is MySQL calls each schema it's own database. This results in having to connect the same DB multiple times to other programs/APIs/inputs which accept JDBC.
-Worse replication options than postgres, not default ACID compliant,
-Don't know the programming term for this... but the horrendous "select col1, col2, col3... colN, count(<field>) from table group by 1" implicit group by. Meaning the system takes your INVALID query, and does things underneath the hood to return a result. Systems should enforce correct syntax (you must group by all non-aggregation columns... mysql implicitly does this under the hood).
-on a tangentially related note to the prior one, MySQL returns null instead of a divide by zero error when you divide by zero. Divide by zero errors are one of the few things that should ALWAYS RETURN AN ERROR NO MATTER WHAT -mysql doesn't support EXCEPT clauses
-doesn't support FULL OUTER JOIN
-doesn't support generate_series,
-poor JSON support
-very limited, poor array/unnest support
-insert VALUES () (in postgres) not supported
-lack of consistent pipe operator concatenation,
-weird datatype suppport and in-query doesn't support ::cast
-doesn't support `select t1._* , t2.field1, t2.field2 from t1 join t2 on t1.id = t2.id` ; that is, you cannot select * from one table, and only certain fields from the other.
-case dependence in field and table names when not escape quoted (mysql uses backtick, postgres uses double quote for escaping names). What the fuck is this? SQL is a case-insensitive language, then the creators build-in case sensitivity?
-As I mentioned above, mysql uses backticks to escape names. This is abnormal for SQL databases.
-mysql LIKE is case-insensitive (what the hell, it's case-sensitive everywhere else). Postgres has LIKE, and ILIKE (insensitive-like).
-ugly and strange support for INTERVAL syntax (intervals, despite being strings, give a syntax error in mysql. Example: In postgres or redshift etc you would right `select current_timestamp - interval '1 week'. In MySQL, you'd have to do `select current_timestamp - interval 1 week` (the '1 week' could be '7 month' or '2 day'... it's a string, and should be in single quotes. MySQL doesn't do this)
-mysql doesn't even support the normal SQL comment of `--`. It uses a `#` instead. No other database does that.
-probably the worst EXPLAIN/EXPLAIN ANALYZE plans I've ever seen from any database, ever
-this is encapsulated in the prior points but you can't do something simple like `select <fields>, row_number() as rownum from table`. Instead you have to declare variables and increment them in the query
-did I mention it's just straight up not SQL standard compliant?
At least MySQL 8.0 supports window functions and CTEs (seriously it's a death knell to a data analyst not to have these). They are the absolute #1 biggest piece of missing functionality to an analyst in my opinion.
This entire post focused on "mySQL have-nots", rather than "Postgres-haves" so I do think there are actually _even more_ advantages to using Postgres over MySQL. I understand MySQL is very fast for writes, but to my understanding it's not even like Postgres is slow for writes, and on the querying side of the coin, it's a universe of difference.
If you ever use MySQL in the future and there will be a data analyst existing somewhere downstream of you, I implore you to use MySQL v8.0 and nothing older, at any cost, for their sake.
Division by zero errors and non-"magical" GROUP BY have been the default mode of operation for a _little_ longer, since the 5.7 series.
I stand by the rest of my points, however.
In other words, while I (or another analyst) would likely still prefer Postgres over MySQL of any version, I wouldn't really have too much to complain about if I was using v8.
- PLV8/PLPython/C functions/etc (with security!)
- TimescaleDB
- Better JSON query support
- Foreign Data Wrappers
- Better window function support
- A richer extension ecosystem (IMO)
Honestly, at this point I wouldn't use MySQL unless you only care about slightly better performance for very simple queries and simpler multi-master scaling/replication. Even saying that, if you don't need that simple multi-master scaling RIGHT NOW, improvements to the Postgres multi-master scaling story are not too far off on the roadmap, so I would still choose PG in that case.
Where I work, we chose MySQL back in 2012 due to production quality async replication. I think (but am never sure) that that is now good in Postgres land.
PG has a lot of SQL features I'd love to use and can't. OTOH MySQL's query planner is predictably dumb, which means I can write queries and have good idea about how well (or not) they'll execute.
Many of the largest tech companies rely on MySQL as their primary data store. They would not do so if it was unreliable with persistence.
There are many valid reasons to choose Postgres over MySQL, or vice versa -- they have different strengths and weaknesses. But there are no major differences regarding data reliability today, nor have there been for many years now.
It isn't fair to compare Postgres-of-today to MySQL-of-over-4-years-ago.
There were other deficiencies mentioned in Klepmann's book on Designing Data apps, but I don't remember the specifics now.
I simply don't see any valid argument for avoiding MySQL due to "data reliability" concerns in 2019.
> There were other minor deficiencies mentioned in Klepmann's book on Designing Data apps, but I don't remember the specifics now.
Well, I can't really respond to non-specific points from a book I haven't read. I'm happy to respond to any specifics re: data reliability concerns, if you want to cite them. FWIW, I have quite extensive expertise on the subject of massive-scale MySQL (16 years of MySQL use; led development of Facebook's internal DBaaS; rebuilt most of Tumblr's backend during its hockey-stick growth period).
Is it still the case?
I believe MariaDB added support for them a couple years earlier, but am not certain.
More broadly, I would agree it's a very painful "gotcha" to have aspects of CREATE TABLE be accepted by the parser but ignored by the engine. However, in MySQL's defense, theoretically this type of flexibility does allow third-party storage engines to support these features if the engine's developer wishes.
Ideally, the engine should throw an error if you try using a feature it does not support, but in a few specific cases it does not (at least for InnoDB). This can be very frustrating, for sure. But at least it's documented. And no database is perfect; they all have similarly-frustrating inconsistencies somewhere.