Winslett

--
-- PostgreSQL database dump
--

--
-- Name: accounts; Type: TABLE; Schema: public; Owner: -
--

CREATE TABLE public.accounts (
    id BIGSERIAL PRIMARY KEY,

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Path: /

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-- Create accounts table with ID, account_name, and a JSONB column for storing addresses
CREATE TABLE accounts (
  id SERIAL PRIMARY KEY,
  name VARCHAR(255),
  addresses JSONB
);

-- Create purchases table with account_id, created_at, and product_name columns
CREATE TABLE purchases (
  purchase_id SERIAL PRIMARY KEY,

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CREATE TABLE public.accounts (
    id SERIAL PRIMARY KEY,
    name character varying,
    created_at timestamp(6) without time zone NOT NULL,
    updated_at timestamp(6) without time zone NOT NULL
);


CREATE TABLE public.carbon_removal_projects (
    id SERIAL PRIMARY KEY,

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CREATE TABLE public.accounts (
    id bigint NOT NULL,
    name character varying,
    created_at timestamp(6) without time zone NOT NULL,
    updated_at timestamp(6) without time zone NOT NULL
);

CREATE SEQUENCE public.accounts_id_seq
    START WITH 1

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--
CREATE TABLE public.employees (
    id bigint NOT NULL,
    first_name character varying,
    last_name character varying,
    start_date timestamp(6) without time zone,
    job_title character varying,
    salary integer,
    manager_id character varying,
    departments jsonb,

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Stick with Simplicity to Cross the Database Financial Gap

TL;DR: keep your data-layer simple, you’ll need the agility it offers when budgets get stretched.

Over the past 12 years of helping people run databases for their businesses, I have discovered that there are only 2 financial phases in the lifecycle of a database:

• Phase 1: Absolute costs are low, thus marginal costs do not matter.
• Phase 2: Absolute costs are high, thus marginal costs are all that matter.

I'm calling this difference between Phase 1 and Phase 2 the "database financial gap."

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SELECT
  total_exec_time,
  mean_exec_time AS avg_ms,
  calls,
  query
FROM pg_stat_statements
ORDER BY mean_exec_time DESC 
LIMIT 10;