select count(*) filter (where year <= 1945) as pre1945, count(*) filter (where year between 1946 and 1964) as period2, count(*) filter (where year between 1965 and 1974) as period3, ... from ... where ...;

ON conflict (member_id) DO UPDATE SET

Writing json_populate_record in the FROM clause is good practice, since all of the extracted columns are available for use without duplicate function calls.

SELECT CNPJ, razaoSocialFROM EmpresaWHERE atividade = '4120-4/00

SELECT E.CNPJ, E.razaoSocialFROM Empresa EWHERE E.CNPJ NOT IN (SELECT T.CNPJFROM Tem TJOIN Caracteristica C ON (T.cod = C.cod)WHERE C.sigla = 'ESG')

Most applications do not need to process massive amounts of data. This has led to a resurgence in data management systems with traditional architectures; SQLite, Postgres, MySQL are all growing strongly, while “NoSQL” and even “NewSQL” systems are stagnating.

Richard was a contractor for Bath Iron Works working on software for the DDG-79 Oscar Austin.

In the Containers pane, expand all nodes of the package container hierarchy, and then clear all check boxes, including the Extract Sample Currency Data check box. Now select the check box for Extract Sample Currency Data to get only the events for this node.

Mysql（多级分销）无限极数据库设计方案（邻接表，路径枚举，嵌套集，闭包表），并提供 嵌套集维持完整性程序 源码和SQL脚本

Hibernate在控制台打印sql语句以及参数

Spring Data JPA中多表联合查询最佳实践

常用的分布式事务解决方案有哪些?

数据库MVCC和隔离级别的关系是什么？

乐观锁和 MVCC 的区别？

SQL 语句的查询结果的的顺序是由哪些因素决定?

如何根据sql语句逆向生成回滚语句？

2020年，java持久层怎么选择？

为什么会有人写出几百行的SQL语句啊？这些人的心态是怎样的？为了凸显自己的强大吗？

现在比较好用的漏洞扫描和注入攻击的工具？

ORM是否必要？

从编程语言设计的角度，如何评价SQL语言？

如何优化加快 SQL 查询速度 ？

Post.in_order_of(:type, %w[Draft Published Archived]).order(:created_at).pluck(:name) which generates SELECT posts.name FROM posts ORDER BY CASE posts.type WHEN 'Draft' THEN 1 WHEN 'Published' THEN 2 WHEN 'Archived' THEN 3 ELSE 4 END ASC, posts.created_at ASC

Many popular SQL clients do not use SSL by default. If you aren’t deliberate about choosing encryption, the connection will be unencrypted.

select * from information_schema.innodb_trx where TIME_TO_SEC(timediff(now(),trx_started))>60

this application uses SQLAlchemy, an ORM library for Python that allows the developer to create schemas and queries using mostly database-independent Python constructs.

How does PostgreSQL access a column? It will fetch the row and then dissect this tuple to calculate the position of the desired column inside the row. So if we want to access column #1000 it means that we have to figure out how long those first 999 columns before our chosen one really are. This can be quite complex. For integer we simply have to add 4, but in case of varchar, the operation turns into something really expensive.

alter column object type jsonb using object::jsonb;

Create the new empty table Write to both old and new table Copy data (in chunks) from old to new Validate consistency Switch reads to new table Stop writes to the old table Cleanup old table

COUNT(*)表示查询所有列的行数，要注意聚合的计算结果虽然是一个数字，但查询的结果仍然是一个二维表，只是这个二维表只有一行一列，并且列名是COUNT(*)

IF NOT EXISTS (SELECT * FROM spatial_ref_sys) THEN ⋮ ANALYZE spatial_ref_sys;

The lateral keyword allows us to access columns after the FROM statement, and reference these columns "earlier" in the query ("earlier" meaning "written higher in the query").

The difference between a non- lateral and a lateral join lies in whether you can look to the left hand table's row.

The comma ( , ) in the FROM clause is short notation for CROSS JOIN . LATERAL is assumed automatically for table functions.

LEFT OUTER JOIN First, an inner join is performed. Then, for each row in T1 that does not satisfy the join condition with any row in T2, a joined row is added with null values in columns of T2. Thus, the joined table always has at least one row for each row in T1.

A LATERAL item can appear at top level in the FROM list, or within a JOIN tree. In the latter case it can also refer to any items that are on the left-hand side of a JOIN that it is on the right-hand side of.

This allows them to reference columns provided by preceding FROM items.

It is often particularly handy to LEFT JOIN to a LATERAL subquery, so that source rows will appear in the result even if the LATERAL subquery produces no rows for them.

Why not just use a join and group by? SELECT AA.ID, COUNT(B.ID) as no_tx, min(B.DATE) as fday_tx, max(B.DATE) as lday_tx, AA.start_date, AA.end_date FROM (SELECT ID, min(DATE) as start_date, max(DATE) as end_date FROM MAIN_TABLE WHERE CODE = 'drugA' GROUP BY ID ) AA LEFT JOIN MAIN_TABLE b ON b.CODE = 'drugB' AND b.DATE > AA.start_date AND b.DATE < AA.end_date GROUP BY AA.ID, AA.start_date, AA.end_date;

window functions: SELECT ID, SUM(CASE WHEN code = 'drugB' THEN 1 ELSE 0 END) as no_tx, MIN(CASE WHEN code = 'drugB' THEN DATE END) as fday_tx, MIN(CASE WHEN code = 'drugB' THEN DATE END) as lday_tx, start_date, end_date FROM (SELECT t.*, MIN(CASE WHEN code = 'drugA' THEN date END) as start_date, MAX(CASE WHEN code = 'drugB' THEN date END) as end_date FROM MAIN_TABLE t ) t WHERE code in ('drugA', 'drugB') AND date between start_date and end_date GROUP BY t.id;

The query to the right of the Lateral would be evaluated for every row of the left table.

SELECT lateral_subquery.* FROM posts JOIN LATERAL ( SELECT comments.* FROM comments WHERE (comments.post_id = posts.id) LIMIT 3 ) lateral_subquery ON true WHERE posts.id

You want the front page to show a few hundred posts along with the top three comments on each post. You’re planning on being very popular, so the front page will need to be very fast. How do you fetch that data efficiently from postgresql using Activerecord?

Making one Comment query per Post is too expensive; it’s N+1 queries (one to fetch the posts, N to fetch the comments). You could use includes to preload all the comments for all the posts, but that requires hydrating hundreds of thousands of records, even though you only need a few hundred for your front page. What you want is some kind of GROUP BY with a LIMIT on each group — but that doesn’t exist, either in Activerecord nor even in postgres. Postgres has a different solution for this problem: the LATERAL JOIN.

Inner Join Venn Diagram

Description of the problem: Select all users from a DB with their parents which have the association by parent_id column. Possible solution: Recursive Common Table Expression.

join = Arel::Nodes::NamedFunction.new('json_b_array_elements', [Arel::Nodes::SqlLiteral.new("subscriptions")]) .as(Arel::Nodes::NamedFunction.new('sd', [Arel::Nodes::SqlLiteral.new("subscription_data")]).to_sql) p = e.project( Arel::Nodes::SqlLiteral.new( Arel::Nodes::Grouping.new( Arel::Nodes::InfixOperation.new('->>', sd[:subscription_data], Arel::Nodes::SqlLiteral.new("'id'"))).to_sql) << '::uuid' ).where( Arel::Nodes::InfixOperation.new('->>', sd[:subscription_data], Arel::Nodes::SqlLiteral.new("'type'").eq( Arel::Nodes::SqlLiteral.new("'Company'") ) ).and(e[:slug].eq(event_slug))) p.join_sources << Arel::Nodes::StringJoin.new( Arel::Nodes::SqlLiteral.new('CROSS JOIN LATERAL')) << join

SELECT "users".* FROM "users" wHERE 'admin' = ANY("users"."roles")

any_role = Arel::Nodes::NamedFunction.new("ANY", [User[:roles]])

lateral (select pledged / fx_rate as pledged_usd) pu, lateral (select pledged_usd / backers_count as avg_pledge_usd) apu, lateral (select goal / fx_rate as goal_usd) gu, lateral (select goal_usd - pledged_usd as usd_from_goal) ufg, lateral (select (deadline - launched_at)/86400.00 as duration) dr

Comment[:article_id].in(Arel.sql(articles_sql))

It's redundant. The transaction commit or rollback can happen in the C# code or the sproc but not both. 

So, what’s a better way to illustrate JOIN operations? JOIN diagrams!

The most important part of this query is the with block. It's a powerful resource, but for this example, you can think of it as a "way to store a variable" that is the path of the contact you need to update, which will be dynamic depending on the record.

It just builds the path as '{1, value}', but we need to convert to text[] because that’s the type expected on the jsonb_path function.

In addition to SQLAlchemy core queries, you can also perform raw SQL queries

query = notes.insert()
values = [
    {"text": "example2", "completed": False},
    {"text": "example3", "completed": True},
]
await database.execute_many(query=query, values=values)

query = "INSERT INTO notes(text, completed) VALUES (:text, :completed)"
values = [
    {"text": "example2", "completed": False},
    {"text": "example3", "completed": True},
]
await database.execute_many(query=query, values=values)

query = notes.select()
rows = await database.fetch_all(query=query)

query = "SELECT * FROM notes WHERE completed = :completed"
rows = await database.fetch_all(query=query, values={"completed": True})

HAVING avg(score) < 0.75 * (SELECT avg(score) FROM performance_reviews)

When you are dealing with an aggregate of aggregates, it needs to be accomplished in two steps. This can be done using a subquery as the FROM clause, essentially giving us a temporary table to then select from, allowing us to find the average of those averages.

Is there a way to select from multiple custom tables using ActiveRecord QueryMethods? I'm trying to replicate this SQL query using Ruby's ActiveRecord Query Methods. select employee.emplid, address.location from (....) employee, (....) address where employee.emplid = address.emplid

over (order by effdt desc) prev

This is an important one, as it will enable us to use the aggregate functions that we are familiar when dealing with relational databases, but in the otherwise counter-intuitive environment of JSON data.

The clean way to call a set-returning function is LEFT [OUTER] JOIN LATERAL. This includes rows without children. To exclude those, change to a [INNER] JOIN LATERAL

FROM test x1 LEFT JOIN test x2 ON x1.id = (x2.data->>'parent')::INT;

Careful, Instead of != you may need to IS DISTINCT FROM operator which also compares NULL value

SELECT * FROM ( -- build virtual table of all hours between -- a date range SELECT start_ts, start_ts + interval '1 hour' AS end_ts FROM generate_series( '2017-03-01'::date, '2017-03-03'::timestamp - interval '1 hour', interval '1 hour' ) AS t(start_ts) ) AS cal LEFT JOIN ( -- build virtual table of uptimes SELECT * FROM ( VALUES ('2017-03-01 01:15:00-06'::timestamp, '2017-03-01 02:15:00-06'::timestamp), ('2017-03-01 08:00:00-06', '2017-03-01 20:00:00-06'), ('2017-03-02 19:00:00-06', null) ) AS t(start_ts, end_ts) ) AS uptime ON cal.end_ts > uptime.start_ts AND cal.start_ts <= coalesce(uptime.end_ts, current_timestamp)

This is not a problem if your DBMS supports SQL recursion: lots of data can be generated with a single query. The WITH RECURSIVE clause comes to the rescue.

Advent of code: SQL + BigQuery

I prefer not to duplicate the name of the table in any of the columns (So I prefer option 1 above).

SQL regular expressions are a curious cross between LIKE notation and common (POSIX) regular expression notation.

The Realm is a new database module that is improving the way databases are used and also supports relationships between objects. If you are part of the SQL development world, then you must be familiar with the Realm.

[:returning] (Postgres-only) An array of attributes that should be returned for all successfully inserted records. For databases that support INSERT ... RETURNING, this will default to returning the primary keys of the successfully inserted records. Pass returning: %w[ id name ] to return the id and name of every successfully inserted record or pass returning: false to omit the clause.

Comparison Time … 🤞

NoSQL databases typically perform better and are easier to scale due to the nature of their data access and storage

WHERE clause introduces a condition on individual rows; HAVING clause introduces a condition on aggregations, i.e. results of selection where a single result, such as count, average, min, max, or sum, has been produced from multiple rows. Your query calls for a second kind of condition (i.e. a condition on an aggregation) hence HAVING works correctly. As a rule of thumb, use WHERE before GROUP BY and HAVING after GROUP BY. It is a rather primitive rule, but it is useful in more than 90% of the cases.

Which database technology to choose

1) Redash and Falcon focus on people that want to do visualizations on top of SQL2) Superset, Tableau and PowerBI focus on people that want to do visualizations with a UI3) Metabase and SeekTable focus on people that want to do quick analysis (they are the closest to an Excel replacement)

Joins are not expensive. Who said it to you? As basically the whole concept of relational databases revolve around joins (from a practical point of view), these product are very good at joining. The normal way of thinking is starting with properly normalized structures and going into fancy denormalizations and similar stuff when the performance really needs it on the reading side. JSON(B) and hstore (and EAV) are good for data with unknown structure.

Another nice SQL script paired with CRON jobs was the one that reminded people of carts that was left for more than 48 hours. Select from cart where state is not empty and last date is more than or equal to 48hrs.... Set this as a CRON that fires at 2AM everyday, period with less activity and traffic. People wake up to emails reminding them about their abandoned carts. Then sit watch magic happens. No AI/ML needed here. Just good 'ol SQL + Bash.

I will write a query like select from order table where last shop date is 3 or greater months. When we get this information, we will send a nice "we miss you, come back and here's X Naira voucher" email. The conversation rate for this one was always greater than 50%.

ACID stands for Atomicity (an operation either succeeds completely or fails, it does not leave partial data), Consistency (once an application performs an operation the results of that operation are visible to it in every subsequent operation), Isolation (an incomplete operation by one user does not cause unexpected side effects for other users), and Durability (once an operation is complete it will be preserved even in the face of machine or system failure).

The SELECT part should not contain any columns not referenced in GROUP BY clause, unless it is wrapped with an aggregate function.

Before SQL3 (1999), the selected fields must appear in the GROUP BY clause

Event.joins(:packages).having('array_agg(packages.type) @> array[?]', packages).group(:id)

Which to use? ANY is a later, more versatile addition, it can be combined with any binary operator returning a boolean value. IN burns down to a special case of ANY. In fact, its second form is rewritten internally: IN is rewritten with = ANY NOT IN is rewritten with <> ALL

Single quotes return text strings.  Double quotes return (if you can really think of them as “returning” anything) identifiers, but with the case preserved.

if you care at all about storing timestamps in MySQL, store them as integers and use the UNIX_TIMESTAMP() and FROM_UNIXTIME() functions.

Database engines in practice don’t actually run queries by joining, and then filtering, and then grouping, because they implement a bunch of optimizations reorder things to make the query run faster as long as reordering things won’t change the results of the query

SELECT isn’t the first thing, it’s like the 5th thing!

DBA Por Acaso: RDS, MySQL e Tuning para Iniciantes