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AlloyDB로 지능형 보안 에이전트 빌드

1. 소개

이 Codelab에서는 PostgreSQL용 AlloyDB와 AI 기능을 사용하여 스마트 컨퍼런스 세션 검색 및 추천 시스템을 빌드합니다. 기존 키워드 검색과 고급 시맨틱 벡터 검색을 결합하고, QueryData를 사용하여 자연어에서 예측 가능한 SQL 문을 생성하고, 지능형 연산자 함수를 활용하는 방법을 알아봅니다.

실습할 내용

AlloyDB 클러스터를 배포하고 AI 기능을 사용 설정합니다.
Conference Dataset을 로드하고 구조를 파악합니다.
AlloyDB Data Access API 사용 설정
ai.if 및 ai.rank과 같은 AlloyDB AI 연산자를 사용하여 시맨틱 작업을 실행합니다.
시맨틱 벡터 (ScaNN)와 텍스트 (RUM) 검색을 결합한 하이브리드 검색을 구현합니다.
AlloyDB의 QueryData 사용 설정
QueryData 템플릿 생성
AI 에이전트와 함께 QueryData 사용

필요한 항목

웹브라우저(예: Chrome)
결제가 사용 설정된 Google Cloud 프로젝트

이 Codelab은 초보자를 포함한 모든 수준의 개발자를 대상으로 합니다.

예상 총 소요 시간: 60~70분 예상 비용: 3달러 미만 (이 Codelab에서 생성된 리소스는 표준 무료 등급 또는 체험판 사용에 해당함).

2. 설정 및 요구사항

프로젝트 설정

Google Cloud 콘솔에 로그인합니다. 아직 Gmail이나 Google Workspace 계정이 없는 경우 계정을 만들어야 합니다.

직장 또는 학교 계정 대신 개인 계정을 사용하세요.

Google Cloud 프로젝트 만들기

Google Cloud 콘솔의 프로젝트 선택기 페이지에서 Google Cloud 프로젝트를 선택하거나 만듭니다.
Cloud 프로젝트에 결제가 사용 설정되어 있는지 확인합니다. 프로젝트에 결제가 사용 설정되어 있는지 확인하는 방법을 알아보세요.

Cloud Shell 시작

Google Cloud를 노트북에서 원격으로 실행할 수 있지만, 이 Codelab에서는 Cloud에서 실행되는 명령줄 환경인 Google Cloud Shell을 사용합니다.

Google Cloud 콘솔 상단에서 Cloud Shell 활성화를 클릭합니다.
인증을 확인합니다.

gcloud auth list

프로젝트를 확인합니다.

gcloud config get project

필요한 경우 설정합니다.

export PROJECT_ID=<YOUR_PROJECT_ID>
gcloud config set project $PROJECT_ID

3. 시작하기 전에

API 사용 설정

다음 명령어를 실행하여 필요한 모든 API를 사용 설정합니다.

gcloud services enable alloydb.googleapis.com \
                       compute.googleapis.com \
                       cloudresourcemanager.googleapis.com \
                       servicenetworking.googleapis.com \
                       aiplatform.googleapis.com \
                       geminidataanalytics.googleapis.com

API 소개

AlloyDB API (alloydb.googleapis.com)를 사용하면 PostgreSQL용 AlloyDB 클러스터를 만들고, 관리하고, 확장할 수 있습니다. 까다로운 엔터프라이즈 트랜잭션 및 분석 워크로드를 위해 설계된 완전 관리형 PostgreSQL 호환 데이터베이스 서비스를 제공합니다.
Compute Engine API (compute.googleapis.com)를 사용하면 가상 머신 (VM), 영구 디스크, 네트워크 설정을 만들고 관리할 수 있습니다. 워크로드를 실행하고 많은 관리형 서비스의 기본 인프라를 호스팅하는 데 필요한 핵심 Infrastructure-as-a-Service (IaaS) 기반을 제공합니다.
Cloud Resource Manager API (cloudresourcemanager.googleapis.com)를 사용하면 Google Cloud 프로젝트의 메타데이터와 구성을 프로그래매틱 방식으로 관리할 수 있습니다. 이를 통해 리소스를 구성하고, ID 및 액세스 관리 (IAM) 정책을 처리하고, 프로젝트 계층 구조 전반에서 권한을 검증할 수 있습니다.
서비스 네트워킹 API (servicenetworking.googleapis.com)를 사용하면 가상 프라이빗 클라우드 (VPC) 네트워크와 Google의 관리형 서비스 간의 비공개 연결 설정을 자동화할 수 있습니다. AlloyDB와 같은 서비스가 다른 리소스와 안전하게 통신할 수 있도록 비공개 IP 액세스를 설정해야 합니다.
Vertex AI API (aiplatform.googleapis.com)를 사용하면 애플리케이션에서 머신러닝 모델을 빌드, 배포, 확장할 수 있습니다. 생성형 AI 모델 (예: Gemini) 및 맞춤 모델 학습에 대한 액세스를 비롯한 모든 Google Cloud AI 서비스의 통합 인터페이스를 제공합니다.
Data Analytics API (geminidataanalytics.googleapis.com)를 사용하면 애플리케이션이 BI 제품에서 일반 AI 기능을 사용할 수 있습니다.

4. AlloyDB 프로비저닝

AlloyDB 클러스터 및 기본 인스턴스를 만듭니다.

비공개 IP 범위 만들기

AlloyDB에는 VPC의 비공개 IP 범위가 필요합니다. default VPC 네트워크를 사용한다고 가정합니다.

비공개 IP 범위를 만듭니다.

gcloud compute addresses create psa-range \
    --global \
    --purpose=VPC_PEERING \
    --prefix-length=24 \
    --description="VPC private service access" \
    --network=default

비공개 연결 설정:

gcloud services vpc-peerings connect \
    --service=servicenetworking.googleapis.com \
    --ranges=psa-range \
    --network=default

AlloyDB 클러스터 만들기

postgres 사용자의 비밀번호를 만듭니다.

export PGPASSWORD=`openssl rand -hex 12`
echo $PGPASSWORD

처음이 아닌 경우 무료 체험판 클러스터 ('TRIAL') 또는 표준 클러스터 ('STANDARD')를 만듭니다.

export REGION=us-central1
export ADBCLUSTER=alloydb-next26-ai-demo-01

gcloud alloydb clusters create $ADBCLUSTER \
    --password=$PGPASSWORD \
    --network=default \
    --region=$REGION \
    --subscription-type=TRIAL

기본 인스턴스를 만듭니다.

gcloud alloydb instances create $ADBCLUSTER-pr \
    --instance-type=PRIMARY \
    --cpu-count=8 \
    --region=$REGION \
    --cluster=$ADBCLUSTER

5. 데이터베이스 권한 설정

Embedding 생성을 위한 Vertex AI 권한 사용 설정

PROJECT_ID=$(gcloud config get-value project)
gcloud projects add-iam-policy-binding $PROJECT_ID \
  --member="serviceAccount:service-$(gcloud projects describe $PROJECT_ID --format="value(projectNumber)")@gcp-sa-alloydb.iam.gserviceaccount.com" \
  --role="roles/aiplatform.user"

데이터 액세스 API 사용 설정

자연어에서 예측 가능한 SQL 문을 만드는 데 도움이 되는 템플릿을 만들기 위해 QueryData 컨텍스트를 사용하려면 AlloyDB 클러스터에서 Data Access API를 사용 설정해야 합니다.

동일한 터미널 탭에서 다음을 실행합니다.

PROJECT_ID=$(gcloud config get-value project)
REGION=us-central1
ADBCLUSTER=alloydb-next26-ai-demo-01
curl -X PATCH \
 -H "Authorization: Bearer $(gcloud auth print-access-token)" \
 -H "Content-Type: application/json" \
 https://alloydb.googleapis.com/v1alpha/projects/$PROJECT_ID/locations/$REGION/clusters/$ADBCLUSTER/instances/$ADBCLUSTER-pr?updateMask=dataApiAccess \
 -d '{
   "dataApiAccess": "ENABLED",
 }'

IAM 인증 사용 설정

에이전트 도구의 경우 인스턴스에서 IAM 인증을 사용 설정한 다음 자신을 사용자로 추가해야 합니다.

동일한 터미널 탭에서 다음을 실행하여 인스턴스에서 IAM을 사용 설정합니다.

PROJECT_ID=$(gcloud config get-value project)
REGION=us-central1
ADBCLUSTER=alloydb-next26-ai-demo-01
gcloud beta alloydb instances update $ADBCLUSTER-pr \
   --database-flags alloydb.iam_authentication=on \
   --region=$REGION \
   --cluster=$ADBCLUSTER \
   --project=$PROJECT_ID \
   --update-mode=FORCE_APPLY

본인을 AlloyDB 사용자로 추가합니다.

REGION=us-central1
ADBCLUSTER=alloydb-next26-ai-demo-01
gcloud alloydb users create $(gcloud config get-value account) \
--cluster=$ADBCLUSTER \
--region=$REGION \
--type=IAM_BASED \
--db-roles=alloydbsuperuser

6. 샘플 데이터베이스 준비

AlloyDB Studio에 연결

Google Cloud 콘솔에서 Postgres용 AlloyDB 페이지로 이동합니다.
기본 인스턴스를 클릭합니다.
왼쪽 탐색에서 AlloyDB Studio를 클릭합니다.
postgres 데이터베이스를 선택합니다.
IAM database authentication(으)로 인증

데이터베이스 만들기

쿼리 편집기에서 다음 SQL을 실행합니다.

CREATE DATABASE conference_db;

다음과 같이 conference_db 데이터베이스로 전환합니다.

AlloyDB studio 화면의 왼쪽 상단에 있는 Current user 버튼을 클릭합니다.
Switch user/database 버튼 클릭
새로 만든 conference_db 데이터베이스를 선택합니다.

pgvector 사용 설정

표준 vector 확장 프로그램이 사용 설정되어 있는지 확인합니다.

CREATE EXTENSION IF NOT EXISTS vector;

샘플 데이터 로드

다음 SQL 스크립트를 실행하여 스키마를 만들고 샘플 데이터로 채웁니다.

1. 이전의 충돌하는 표 정리

DROP TABLE IF EXISTS public.attendees_sessions CASCADE;
DROP TABLE IF EXISTS public.session_speaker_mapping CASCADE;
DROP TABLE IF EXISTS public.session_topic_mapping CASCADE;
DROP TABLE IF EXISTS public.session CASCADE;
DROP TABLE IF EXISTS public.attendees CASCADE;
DROP TABLE IF EXISTS public.speaker CASCADE;
DROP TABLE IF EXISTS public.session_topic CASCADE;

2. 테이블 만들기

CREATE TABLE public.attendees (
    username character varying(100) NOT NULL PRIMARY KEY,
    name character varying(100),
    company character varying(150),
    job_title character varying(100),
    area_of_interest character varying(100),
    street_address text,
    city character varying(100),
    state_province character varying(100),
    country character varying(100),
    created_at timestamp without time zone DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE public.attendees_sessions (
    username character varying(100) NOT NULL REFERENCES public.attendees(username),
    session_id character varying(50) NOT NULL,
    registration_date timestamp without time zone DEFAULT CURRENT_TIMESTAMP,
    PRIMARY KEY (username, session_id)
);

CREATE TABLE public.session (
    session_id character varying(50) NOT NULL PRIMARY KEY,
    session_name character varying(255) NOT NULL,
    full_description text,
    full_description_embedding vector(768) GENERATED ALWAYS AS (embedding('text-embedding-005', full_description)) STORED,
    description_tsvector tsvector GENERATED ALWAYS AS (to_tsvector('english', full_description)) STORED,
    session_format character varying(50),
    learning_level character varying(50),
    session_url character varying(255),
    for_job_role character varying(250),
    session_date date,
    session_start_time time without time zone,
    session_end_time time without time zone,
    session_location character varying(100),
    capacity integer,
    remaining_capacity integer,
    interest_area character varying(100),
    industry character varying(100)
);

COMMENT ON COLUMN public.session.session_format IS 'The format of a session. Possible values are Keynotes, Breakouts, Lightning Talks.';

CREATE TABLE public.speaker (
    speaker_id integer NOT NULL PRIMARY KEY,
    speaker_name character varying(100) NOT NULL,
    job_title character varying(100),
    company character varying(100),
    type character varying(50),
    profile_pic character varying(100),
    speaker_url character varying(100)
);

CREATE TABLE public.session_speaker_mapping (
    session_id character varying(50) NOT NULL REFERENCES public.session(session_id),
    speaker_id integer NOT NULL REFERENCES public.speaker(speaker_id),
    PRIMARY KEY (session_id, speaker_id)
);

CREATE TABLE public.session_topic (
    topic character varying(100) NOT NULL PRIMARY KEY,
    topic_desc text,
    embedding vector(768) GENERATED ALWAYS AS (embedding('text-embedding-005', topic_desc)) STORED
);

CREATE TABLE public.session_topic_mapping (
    session_id character varying(50) NOT NULL REFERENCES public.session(session_id),
    topic character varying(100) NOT NULL REFERENCES public.session_topic(topic),
    PRIMARY KEY (session_id, topic)
);

3. 샘플 데이터로 테이블 채우기

3.1 세션 주제 채우기

-- Insert Topics
INSERT INTO public.session_topic (topic, topic_desc) VALUES
('Databases', 'Relational and non-relational database technologies.'),
('AI & Machine Learning', 'Generative AI, LLMs, and ML infrastructure.'),
('Cloud Architecture', 'Designing scalable and resilient cloud systems.'),
('Security', 'Identity, compliance, and network security.'),
('DevOps', 'CI/CD, platform engineering, and automation.') ON CONFLICT DO NOTHING;

3.2 스피커 채우기

-- Insert Speakers
INSERT INTO public.speaker (speaker_id, speaker_name, job_title, company, type, profile_pic, speaker_url) VALUES
(1, 'Speaker 1', 'Director of Engineering', 'DeepMind', 'External', 'pic_1.png', 'http://speakers.com/1'),
(2, 'Speaker 2', 'Cloud Architect', 'Verily', 'External', 'pic_2.png', 'http://speakers.com/2'),
(3, 'Speaker 3', 'Product Manager', 'GlobalEnterprises', 'External', 'pic_3.png', 'http://speakers.com/3'),
(4, 'Speaker 4', 'Tech Lead', 'Google', 'Internal', 'pic_4.png', 'http://speakers.com/4'),
(5, 'Speaker 5', 'Security Engineer', 'DeepMind', 'External', 'pic_5.png', 'http://speakers.com/5'),
(6, 'Speaker 6', 'DevOps Engineer', 'DeepMind', 'External', 'pic_6.png', 'http://speakers.com/6'),
(7, 'Speaker 7', 'Product Manager', 'DataSystems', 'External', 'pic_7.png', 'http://speakers.com/7'),
(8, 'Speaker 8', 'Director of Engineering', 'Google', 'Internal', 'pic_8.png', 'http://speakers.com/8'),
(9, 'Speaker 9', 'Cloud Architect', 'Google', 'Internal', 'pic_9.png', 'http://speakers.com/9'),
(10, 'Speaker 10', 'DevOps Engineer', 'Alphabet', 'Internal', 'pic_10.png', 'http://speakers.com/10'),
(11, 'Speaker 11', 'Tech Lead', 'Alphabet', 'External', 'pic_11.png', 'http://speakers.com/11'),
(12, 'Speaker 12', 'Product Manager', 'Alphabet', 'External', 'pic_12.png', 'http://speakers.com/12'),
(13, 'Speaker 13', 'Tech Lead', 'SoftSolutions', 'Internal', 'pic_13.png', 'http://speakers.com/13'),
(14, 'Speaker 14', 'Director of Engineering', 'Verily', 'Internal', 'pic_14.png', 'http://speakers.com/14'),
(15, 'Speaker 15', 'DevOps Engineer', 'CloudNative', 'External', 'pic_15.png', 'http://speakers.com/15'),
(16, 'Speaker 16', 'Software Engineer', 'Waymo', 'External', 'pic_16.png', 'http://speakers.com/16'),
(17, 'Speaker 17', 'DevOps Engineer', 'Google', 'External', 'pic_17.png', 'http://speakers.com/17'),
(18, 'Speaker 18', 'Data Scientist', 'GlobalEnterprises', 'External', 'pic_18.png', 'http://speakers.com/18'),
(19, 'Speaker 19', 'Security Engineer', 'Waymo', 'Internal', 'pic_19.png', 'http://speakers.com/19'),
(20, 'Speaker 20', 'Tech Lead', 'SoftSolutions', 'Internal', 'pic_20.png', 'http://speakers.com/20') ON CONFLICT (speaker_id) DO NOTHING;

3.3 참석자 채우기

-- Insert Attendees
INSERT INTO public.attendees (username, name, company, job_title, area_of_interest, street_address, city, state_province, country) VALUES
('user_1', 'Attendee 1', 'GlobalEnterprises', 'Data Scientist', 'DevOps', '1 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_2', 'Attendee 2', 'Alphabet', 'Director of Engineering', 'DevOps', '2 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_3', 'Attendee 3', 'CloudNative', 'Data Scientist', 'AI & Machine Learning', '3 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_4', 'Attendee 4', 'SoftSolutions', 'Product Manager', 'DevOps', '4 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_5', 'Attendee 5', 'DataSystems', 'Software Engineer', 'DevOps', '5 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_6', 'Attendee 6', 'Google', 'Director of Engineering', 'Security', '6 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_7', 'Attendee 7', 'Waymo', 'Product Manager', 'AI & Machine Learning', '7 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_8', 'Attendee 8', 'Verily', 'Product Manager', 'DevOps', '8 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_9', 'Attendee 9', 'Waymo', 'Software Engineer', 'DevOps', '9 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_10', 'Attendee 10', 'Waymo', 'DevOps Engineer', 'Databases', '10 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_11', 'Attendee 11', 'GlobalEnterprises', 'Tech Lead', 'Cloud Architecture', '11 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_12', 'Attendee 12', 'Google', 'Tech Lead', 'DevOps', '12 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_13', 'Attendee 13', 'DeepMind', 'Software Engineer', 'Cloud Architecture', '13 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_14', 'Attendee 14', 'DataSystems', 'Security Engineer', 'DevOps', '14 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_15', 'Attendee 15', 'DeepMind', 'Security Engineer', 'Cloud Architecture', '15 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_16', 'Attendee 16', 'Verily', 'Security Engineer', 'Cloud Architecture', '16 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_17', 'Attendee 17', 'Google', 'Software Engineer', 'Cloud Architecture', '17 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_18', 'Attendee 18', 'GlobalEnterprises', 'Security Engineer', 'DevOps', '18 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_19', 'Attendee 19', 'CloudNative', 'Data Scientist', 'Databases', '19 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_20', 'Attendee 20', 'DataSystems', 'Cloud Architect', 'AI & Machine Learning', '20 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_21', 'Attendee 21', 'Google', 'Data Scientist', 'Databases', '21 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_22', 'Attendee 22', 'TechCorp', 'Data Scientist', 'DevOps', '22 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_23', 'Attendee 23', 'DeepMind', 'Product Manager', 'Databases', '23 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_24', 'Attendee 24', 'Google', 'Cloud Architect', 'AI & Machine Learning', '24 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_25', 'Attendee 25', 'GlobalEnterprises', 'Software Engineer', 'AI & Machine Learning', '25 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_26', 'Attendee 26', 'Verily', 'Security Engineer', 'Security', '26 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_27', 'Attendee 27', 'GlobalEnterprises', 'Tech Lead', 'Databases', '27 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_28', 'Attendee 28', 'Google', 'Tech Lead', 'Databases', '28 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_29', 'Attendee 29', 'SoftSolutions', 'Software Engineer', 'Databases', '29 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_30', 'Attendee 30', 'Verily', 'DevOps Engineer', 'Databases', '30 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_31', 'Attendee 31', 'DeepMind', 'Data Scientist', 'Databases', '31 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_32', 'Attendee 32', 'SoftSolutions', 'Software Engineer', 'Cloud Architecture', '32 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_33', 'Attendee 33', 'DeepMind', 'Tech Lead', 'Databases', '33 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_34', 'Attendee 34', 'Alphabet', 'Security Engineer', 'DevOps', '34 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_35', 'Attendee 35', 'TechCorp', 'DevOps Engineer', 'Cloud Architecture', '35 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_36', 'Attendee 36', 'DataSystems', 'Director of Engineering', 'Security', '36 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_37', 'Attendee 37', 'Google', 'Cloud Architect', 'Databases', '37 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_38', 'Attendee 38', 'Google', 'Product Manager', 'Security', '38 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_39', 'Attendee 39', 'SoftSolutions', 'Security Engineer', 'Cloud Architecture', '39 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_40', 'Attendee 40', 'Waymo', 'Director of Engineering', 'Cloud Architecture', '40 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_41', 'Attendee 41', 'SoftSolutions', 'Tech Lead', 'Security', '41 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_42', 'Attendee 42', 'DeepMind', 'Cloud Architect', 'Security', '42 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_43', 'Attendee 43', 'TechCorp', 'Cloud Architect', 'Databases', '43 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_44', 'Attendee 44', 'Alphabet', 'Security Engineer', 'AI & Machine Learning', '44 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_45', 'Attendee 45', 'CloudNative', 'Cloud Architect', 'AI & Machine Learning', '45 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_46', 'Attendee 46', 'CloudNative', 'Data Scientist', 'Security', '46 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_47', 'Attendee 47', 'DeepMind', 'Data Scientist', 'Security', '47 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_48', 'Attendee 48', 'Verily', 'DevOps Engineer', 'AI & Machine Learning', '48 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_49', 'Attendee 49', 'Verily', 'Director of Engineering', 'AI & Machine Learning', '49 Main St', 'CityVille', 'StateName', 'CountryName'),
('user_50', 'Attendee 50', 'Google', 'Director of Engineering', 'DevOps', '50 Main St', 'CityVille', 'StateName', 'CountryName') ON CONFLICT (username) DO NOTHING;

3.4 세션 채우기

-- Insert Sessions
INSERT INTO public.session (session_id, session_name, full_description, session_format, learning_level, session_url, for_job_role, session_date, session_start_time, session_end_time, session_location, capacity, remaining_capacity, interest_area, industry) VALUES
('S001', 'AlloyDB Deep Dive: Advanced Performance Tuning', 'Learn how to squeeze every drop of performance out of AlloyDB. This session covers index tuning, memory management, and advanced query optimization techniques.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S001', 'Cloud Architect', '2026-04-11', '15:00:00', '15:00:00', 'Room C', 500, 192, 'Databases', 'Technology'),
('S002', 'Vector Search at Scale with ScaNN in AlloyDB', 'Discover how to use ScaNN for fast approximate nearest neighbor search in AlloyDB. Perfect for building high-scale recommendation systems.', 'Breakouts', 'Intermediate', 'http://sessions.com/S002', 'Cloud Architect', '2026-04-10', '14:00:00', '14:00:00', 'Auditorium', 100, 16, 'Databases', 'Retail'),
('S003', 'Building Gen AI Apps with Vertex AI and AlloyDB', 'A practical guide to integrating Vertex AI embeddings and LLMs with your operational data in AlloyDB to build intelligent applications.', 'Lightning Talks', 'Advanced', 'http://sessions.com/S003', 'Director of Engineering', '2026-04-11', '11:00:00', '11:00:00', 'Auditorium', 50, 28, 'Databases', 'Healthcare'),
('S004', 'Spanner: Architecting for Global Consistency', 'Learn how Spanner achieves global scale without sacrificing strong consistency. We will cover multi-region deployment patterns.', 'Keynotes', 'Intermediate', 'http://sessions.com/S004', 'Cloud Architect', '2026-04-11', '11:00:00', '11:00:00', 'Room B', 500, 441, 'Databases', 'Healthcare'),
('S005', 'BigQuery + Vertex AI: Predictive Analytics Made Easy', 'See how to combine the power of BigQuery for data warehousing with Vertex AI for machine learning to build predictive models directly on your data.', 'Breakouts', 'Advanced', 'http://sessions.com/S005', 'Product Manager', '2026-04-11', '14:00:00', '14:00:00', 'Room C', 100, 96, 'AI & Machine Learning', 'Retail'),
('S006', 'Securing Your Data: Best Practices in AlloyDB', 'Deep dive into the security features of AlloyDB, including IAM integration, encryption at rest and in transit, and audit logging.', 'Keynotes', 'Beginner', 'http://sessions.com/S006', 'Product Manager', '2026-04-09', '15:00:00', '15:00:00', 'Room C', 200, 199, 'Databases', 'Healthcare'),
('S007', 'Kubernetes for Databases: Running PostgreSQL on GKE', 'Best practices for running stateful workloads like PostgreSQL on Google Kubernetes Engine (GKE).', 'Lightning Talks', 'Intermediate', 'http://sessions.com/S007', 'Software Engineer', '2026-04-09', '16:00:00', '16:00:00', 'Room C', 100, 63, 'Databases', 'Retail'),
('S008', 'Real-time Analytics with BigQuery and Pub/Sub', 'Learn how to build real-time data pipelines using Pub/Sub and stream data directly into BigQuery for instant insights.', 'Keynotes', 'Beginner', 'http://sessions.com/S008', 'Security Engineer', '2026-04-10', '14:00:00', '14:00:00', 'Room C', 200, 65, 'Databases', 'Finance'),
('S009', 'Microservices Architecture with Spanner', 'How to design microservices that leverage Spanner''s unique capabilities for distributed transactions and scalability.', 'Keynotes', 'Beginner', 'http://sessions.com/S009', 'Software Engineer', '2026-04-11', '10:00:00', '10:00:00', 'Auditorium', 100, 25, 'Databases', 'Technology'),
('S010', 'AI-Powered Search with ScaNN and LLMs', 'Learn how to combine ScaNN vector search with Large Language Models to create powerful, context-aware search experiences.', 'Keynotes', 'Intermediate', 'http://sessions.com/S010', 'Tech Lead', '2026-04-11', '11:00:00', '11:00:00', 'Room C', 50, 27, 'AI & Machine Learning', 'Healthcare'),
('S011', 'AlloyDB Omni: Run AlloyDB Anywhere', 'Explore AlloyDB Omni, the downloadable edition of AlloyDB that lets you run the same high-performance database in your own data center or on the edge.', 'Breakouts', 'Intermediate', 'http://sessions.com/S011', 'Cloud Architect', '2026-04-09', '10:00:00', '10:00:00', 'Room B', 200, 195, 'Databases', 'Technology'),
('S012', 'Data Mesh on Google Cloud: Best Practices', 'How to implement a decentralized data mesh architecture using BigQuery, Dataplex, and other Google Cloud tools.', 'Lightning Talks', 'Intermediate', 'http://sessions.com/S012', 'Data Scientist', '2026-04-10', '16:00:00', '16:00:00', 'Room C', 100, 46, 'Cloud Architecture', 'Technology'),
('S013', 'Serverless Databases: When to use Cloud SQL vs AlloyDB', 'A comparison of Cloud SQL and AlloyDB, helping you choose the right database for your serverless and traditional applications.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S013', 'DevOps Engineer', '2026-04-10', '11:00:00', '11:00:00', 'Room C', 500, 458, 'Databases', 'Manufacturing'),
('S014', 'Graph Databases on Google Cloud', 'Explore options for graph data processing on Google Cloud, including integrations with existing database services.', 'Breakouts', 'Advanced', 'http://sessions.com/S014', 'DevOps Engineer', '2026-04-11', '09:00:00', '09:00:00', 'Room C', 500, 338, 'DevOps', 'Finance'),
('S015', 'Automating DB Ops with Gemini', 'See how Gemini can help DBA and developers write better SQL, optimize queries, and manage database infrastructure.', 'Breakouts', 'Advanced', 'http://sessions.com/S015', 'Cloud Architect', '2026-04-11', '14:00:00', '14:00:00', 'Room C', 100, 53, 'AI & Machine Learning', 'Retail'),
('S016', 'High Availability and Disaster Recovery in AlloyDB', 'A deep dive into how AlloyDB ensures your data is always available, covering failover mechanisms and backup strategies.', 'Breakouts', 'Advanced', 'http://sessions.com/S016', 'Cloud Architect', '2026-04-11', '11:00:00', '11:00:00', 'Room A', 200, 52, 'Databases', 'Technology'),
('S017', 'Optimizing Costs in BigQuery', 'Practical tips for reducing your BigQuery bill without sacrificing performance, covering slot management and query optimization.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S017', 'Security Engineer', '2026-04-11', '15:00:00', '15:00:00', 'Room B', 100, 65, 'Databases', 'Manufacturing'),
('S018', 'Continuous Integration for Database Schemas', 'How to apply CI/CD principles to database schema changes using tools like Liquibase or Flyway on Google Cloud.', 'Breakouts', 'Beginner', 'http://sessions.com/S018', 'Cloud Architect', '2026-04-11', '09:00:00', '09:00:00', 'Room B', 50, 30, 'Security', 'Technology'),
('S019', 'Data Governance in the Age of AI', 'Learn how to maintain data quality, privacy, and compliance when feeding enterprise data into AI models.', 'Breakouts', 'Beginner', 'http://sessions.com/S019', 'Product Manager', '2026-04-10', '10:00:00', '10:00:00', 'Room A', 100, 39, 'DevOps', 'Retail'),
('S020', 'Hybrid Search: Combining Vector and Keyword Search', 'Learn how to implement hybrid search in AlloyDB to get the best of both worlds: semantic understanding and precise keyword matching.', 'Breakouts', 'Intermediate', 'http://sessions.com/S020', 'Data Scientist', '2026-04-09', '14:00:00', '14:00:00', 'Auditorium', 500, 176, 'Databases', 'Manufacturing'),
('S021', 'Deep Dive into ScaNN for High Availability', 'Join this session to explore how ScaNN can be used for High Availability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Advanced', 'http://sessions.com/S021', 'Software Engineer', '2026-04-11', '14:00:00', '14:00:00', 'Room B', 200, 18, 'AI & Machine Learning', 'Finance'),
('S022', 'Understanding Vertex AI for Modernization', 'Join this session to explore how Vertex AI can be used for Modernization. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S022', 'Tech Lead', '2026-04-09', '09:00:00', '09:00:00', 'Room A', 50, 48, 'AI & Machine Learning', 'Finance'),
('S023', 'Securing Vertex AI for Modernization', 'Join this session to explore how Vertex AI can be used for Modernization. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Intermediate', 'http://sessions.com/S023', 'Tech Lead', '2026-04-10', '10:00:00', '10:00:00', 'Auditorium', 200, 96, 'AI & Machine Learning', 'Manufacturing'),
('S024', 'Mastering Kubernetes for Security', 'Join this session to explore how Kubernetes can be used for Security. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S024', 'DevOps Engineer', '2026-04-09', '09:00:00', '09:00:00', 'Auditorium', 200, 160, 'Security', 'Technology'),
('S025', 'Mastering AlloyDB for Modernization', 'Join this session to explore how AlloyDB can be used for Modernization. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Advanced', 'http://sessions.com/S025', 'Director of Engineering', '2026-04-11', '16:00:00', '16:00:00', 'Room B', 200, 74, 'Databases', 'Healthcare'),
('S026', 'Securing Vertex AI for Automation', 'Join this session to explore how Vertex AI can be used for Automation. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Advanced', 'http://sessions.com/S026', 'Cloud Architect', '2026-04-11', '16:00:00', '16:00:00', 'Room A', 50, 25, 'AI & Machine Learning', 'Manufacturing'),
('S027', 'Mastering BigQuery for Analytics', 'Join this session to explore how BigQuery can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Intermediate', 'http://sessions.com/S027', 'Security Engineer', '2026-04-11', '10:00:00', '10:00:00', 'Room B', 200, 117, 'Databases', 'Healthcare'),
('S028', 'Deep Dive into Spanner for Integration', 'Join this session to explore how Spanner can be used for Integration. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Advanced', 'http://sessions.com/S028', 'Tech Lead', '2026-04-09', '10:00:00', '10:00:00', 'Auditorium', 50, 32, 'Databases', 'Retail'),
('S029', 'Scaling Generative AI for Automation', 'Join this session to explore how Generative AI can be used for Automation. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Intermediate', 'http://sessions.com/S029', 'Tech Lead', '2026-04-09', '10:00:00', '10:00:00', 'Room A', 200, 157, 'AI & Machine Learning', 'Healthcare'),
('S030', 'Deploying Generative AI for Analytics', 'Join this session to explore how Generative AI can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Advanced', 'http://sessions.com/S030', 'Director of Engineering', '2026-04-09', '16:00:00', '16:00:00', 'Room B', 500, 462, 'DevOps', 'Healthcare'),
('S031', 'Mastering ScaNN for High Availability', 'Join this session to explore how ScaNN can be used for High Availability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S031', 'Director of Engineering', '2026-04-10', '09:00:00', '09:00:00', 'Room C', 500, 140, 'AI & Machine Learning', 'Finance'),
('S032', 'Deploying Kubernetes for Integration', 'Join this session to explore how Kubernetes can be used for Integration. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Advanced', 'http://sessions.com/S032', 'Cloud Architect', '2026-04-11', '14:00:00', '14:00:00', 'Auditorium', 200, 22, 'Databases', 'Retail'),
('S033', 'Securing Gemini for Performance', 'Join this session to explore how Gemini can be used for Performance. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Intermediate', 'http://sessions.com/S033', 'Cloud Architect', '2026-04-09', '16:00:00', '16:00:00', 'Room B', 200, 84, 'AI & Machine Learning', 'Technology'),
('S034', 'Deploying Vertex AI for Scale', 'Join this session to explore how Vertex AI can be used for Scale. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Intermediate', 'http://sessions.com/S034', 'Director of Engineering', '2026-04-10', '10:00:00', '10:00:00', 'Room B', 500, 222, 'AI & Machine Learning', 'Finance'),
('S035', 'Deep Dive into BigQuery for Analytics', 'Join this session to explore how BigQuery can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Intermediate', 'http://sessions.com/S035', 'Security Engineer', '2026-04-11', '15:00:00', '15:00:00', 'Room A', 500, 408, 'Databases', 'Healthcare'),
('S036', 'Deploying Kubernetes for Security', 'Join this session to explore how Kubernetes can be used for Security. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Advanced', 'http://sessions.com/S036', 'Tech Lead', '2026-04-10', '09:00:00', '09:00:00', 'Room A', 200, 54, 'Security', 'Healthcare'),
('S037', 'Understanding Spanner for Integration', 'Join this session to explore how Spanner can be used for Integration. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S037', 'Tech Lead', '2026-04-10', '14:00:00', '14:00:00', 'Room C', 200, 89, 'Databases', 'Technology'),
('S038', 'Deep Dive into Kubernetes for Automation', 'Join this session to explore how Kubernetes can be used for Automation. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S038', 'Product Manager', '2026-04-11', '09:00:00', '09:00:00', 'Room C', 50, 47, 'AI & Machine Learning', 'Healthcare'),
('S039', 'Optimizing BigQuery for Reliability', 'Join this session to explore how BigQuery can be used for Reliability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Beginner', 'http://sessions.com/S039', 'Director of Engineering', '2026-04-10', '14:00:00', '14:00:00', 'Auditorium', 200, 38, 'Databases', 'Retail'),
('S040', 'Deep Dive into Spanner for Cost Efficiency', 'Join this session to explore how Spanner can be used for Cost Efficiency. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Advanced', 'http://sessions.com/S040', 'Software Engineer', '2026-04-09', '14:00:00', '14:00:00', 'Auditorium', 50, 2, 'Databases', 'Healthcare'),
('S041', 'Exploring Gemini for Cost Efficiency', 'Join this session to explore how Gemini can be used for Cost Efficiency. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Intermediate', 'http://sessions.com/S041', 'Product Manager', '2026-04-10', '15:00:00', '15:00:00', 'Room A', 100, 73, 'AI & Machine Learning', 'Healthcare'),
('S042', 'Mastering ScaNN for Performance', 'Join this session to explore how ScaNN can be used for Performance. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S042', 'Tech Lead', '2026-04-09', '11:00:00', '11:00:00', 'Auditorium', 100, 50, 'AI & Machine Learning', 'Manufacturing'),
('S043', 'Mastering ScaNN for Analytics', 'Join this session to explore how ScaNN can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Advanced', 'http://sessions.com/S043', 'Data Scientist', '2026-04-10', '09:00:00', '09:00:00', 'Room B', 200, 111, 'AI & Machine Learning', 'Technology'),
('S044', 'Securing Gemini for Integration', 'Join this session to explore how Gemini can be used for Integration. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Advanced', 'http://sessions.com/S044', 'Cloud Architect', '2026-04-09', '14:00:00', '14:00:00', 'Auditorium', 500, 281, 'AI & Machine Learning', 'Healthcare'),
('S045', 'Architecting Cloud SQL for Integration', 'Join this session to explore how Cloud SQL can be used for Integration. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Advanced', 'http://sessions.com/S045', 'Product Manager', '2026-04-11', '11:00:00', '11:00:00', 'Room C', 50, 6, 'AI & Machine Learning', 'Retail'),
('S046', 'Securing Kubernetes for Scale', 'Join this session to explore how Kubernetes can be used for Scale. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S046', 'Security Engineer', '2026-04-11', '14:00:00', '14:00:00', 'Room B', 100, 80, 'AI & Machine Learning', 'Manufacturing'),
('S047', 'Mastering PostgreSQL for Integration', 'Join this session to explore how PostgreSQL can be used for Integration. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Advanced', 'http://sessions.com/S047', 'Tech Lead', '2026-04-10', '14:00:00', '14:00:00', 'Auditorium', 200, 134, 'Databases', 'Manufacturing'),
('S048', 'Deep Dive into Kubernetes for Cost Efficiency', 'Join this session to explore how Kubernetes can be used for Cost Efficiency. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Intermediate', 'http://sessions.com/S048', 'Data Scientist', '2026-04-11', '09:00:00', '09:00:00', 'Room C', 500, 102, 'AI & Machine Learning', 'Retail'),
('S049', 'Deep Dive into Cloud SQL for Scale', 'Join this session to explore how Cloud SQL can be used for Scale. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S049', 'Security Engineer', '2026-04-09', '14:00:00', '14:00:00', 'Auditorium', 200, 127, 'AI & Machine Learning', 'Healthcare'),
('S050', 'Scaling Cloud SQL for Reliability', 'Join this session to explore how Cloud SQL can be used for Reliability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Intermediate', 'http://sessions.com/S050', 'Software Engineer', '2026-04-10', '15:00:00', '15:00:00', 'Room A', 100, 40, 'Cloud Architecture', 'Finance'),
('S051', 'Building Vertex AI for Security', 'Join this session to explore how Vertex AI can be used for Security. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Beginner', 'http://sessions.com/S051', 'Director of Engineering', '2026-04-09', '15:00:00', '15:00:00', 'Room C', 500, 138, 'AI & Machine Learning', 'Retail'),
('S052', 'Exploring Vertex AI for Modernization', 'Join this session to explore how Vertex AI can be used for Modernization. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Intermediate', 'http://sessions.com/S052', 'DevOps Engineer', '2026-04-09', '09:00:00', '09:00:00', 'Room C', 200, 27, 'AI & Machine Learning', 'Finance'),
('S053', 'Optimizing Generative AI for Automation', 'Join this session to explore how Generative AI can be used for Automation. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Beginner', 'http://sessions.com/S053', 'Director of Engineering', '2026-04-10', '11:00:00', '11:00:00', 'Room C', 500, 65, 'Databases', 'Healthcare'),
('S054', 'Architecting PostgreSQL for Performance', 'Join this session to explore how PostgreSQL can be used for Performance. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S054', 'Data Scientist', '2026-04-09', '11:00:00', '11:00:00', 'Auditorium', 200, 103, 'Databases', 'Healthcare'),
('S055', 'Mastering Generative AI for Automation', 'Join this session to explore how Generative AI can be used for Automation. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Intermediate', 'http://sessions.com/S055', 'Data Scientist', '2026-04-11', '11:00:00', '11:00:00', 'Room C', 50, 31, 'AI & Machine Learning', 'Technology'),
('S056', 'Exploring ScaNN for Reliability', 'Join this session to explore how ScaNN can be used for Reliability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Intermediate', 'http://sessions.com/S056', 'Security Engineer', '2026-04-11', '15:00:00', '15:00:00', 'Room A', 500, 351, 'AI & Machine Learning', 'Healthcare'),
('S057', 'Deep Dive into BigQuery for Performance', 'Join this session to explore how BigQuery can be used for Performance. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Intermediate', 'http://sessions.com/S057', 'DevOps Engineer', '2026-04-11', '11:00:00', '11:00:00', 'Room B', 200, 200, 'Databases', 'Finance'),
('S058', 'Scaling Generative AI for Reliability', 'Join this session to explore how Generative AI can be used for Reliability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S058', 'Cloud Architect', '2026-04-10', '11:00:00', '11:00:00', 'Room C', 100, 61, 'Databases', 'Finance'),
('S059', 'Securing Vertex AI for High Availability', 'Join this session to explore how Vertex AI can be used for High Availability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Beginner', 'http://sessions.com/S059', 'Data Scientist', '2026-04-11', '16:00:00', '16:00:00', 'Room B', 500, 224, 'AI & Machine Learning', 'Manufacturing'),
('S060', 'Architecting AlloyDB for Integration', 'Join this session to explore how AlloyDB can be used for Integration. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S060', 'Tech Lead', '2026-04-09', '14:00:00', '14:00:00', 'Room A', 500, 223, 'Databases', 'Technology'),
('S061', 'Understanding Spanner for High Availability', 'Join this session to explore how Spanner can be used for High Availability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Advanced', 'http://sessions.com/S061', 'Tech Lead', '2026-04-09', '16:00:00', '16:00:00', 'Room C', 100, 52, 'Databases', 'Manufacturing'),
('S062', 'Scaling Kubernetes for Cost Efficiency', 'Join this session to explore how Kubernetes can be used for Cost Efficiency. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Advanced', 'http://sessions.com/S062', 'Product Manager', '2026-04-10', '16:00:00', '16:00:00', 'Room B', 100, 0, 'Cloud Architecture', 'Retail'),
('S063', 'Architecting Kubernetes for High Availability', 'Join this session to explore how Kubernetes can be used for High Availability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S063', 'Security Engineer', '2026-04-09', '10:00:00', '10:00:00', 'Auditorium', 200, 0, 'Security', 'Finance'),
('S064', 'Mastering Gemini for Integration', 'Join this session to explore how Gemini can be used for Integration. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Advanced', 'http://sessions.com/S064', 'Security Engineer', '2026-04-10', '15:00:00', '15:00:00', 'Room B', 100, 8, 'AI & Machine Learning', 'Technology'),
('S065', 'Optimizing Generative AI for Security', 'Join this session to explore how Generative AI can be used for Security. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S065', 'Tech Lead', '2026-04-09', '15:00:00', '15:00:00', 'Room C', 500, 230, 'AI & Machine Learning', 'Retail'),
('S066', 'Optimizing BigQuery for Scale', 'Join this session to explore how BigQuery can be used for Scale. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Intermediate', 'http://sessions.com/S066', 'Tech Lead', '2026-04-11', '10:00:00', '10:00:00', 'Room C', 500, 186, 'Databases', 'Healthcare'),
('S067', 'Scaling PostgreSQL for Automation', 'Join this session to explore how PostgreSQL can be used for Automation. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Beginner', 'http://sessions.com/S067', 'Product Manager', '2026-04-10', '11:00:00', '11:00:00', 'Auditorium', 100, 17, 'Databases', 'Retail'),
('S068', 'Exploring AlloyDB for Automation', 'Join this session to explore how AlloyDB can be used for Automation. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S068', 'Director of Engineering', '2026-04-11', '16:00:00', '16:00:00', 'Room B', 100, 77, 'Databases', 'Retail'),
('S069', 'Securing BigQuery for Cost Efficiency', 'Join this session to explore how BigQuery can be used for Cost Efficiency. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Advanced', 'http://sessions.com/S069', 'Cloud Architect', '2026-04-09', '14:00:00', '14:00:00', 'Room A', 500, 437, 'Databases', 'Manufacturing'),
('S070', 'Deep Dive into Kubernetes for Integration', 'Join this session to explore how Kubernetes can be used for Integration. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Intermediate', 'http://sessions.com/S070', 'Cloud Architect', '2026-04-11', '11:00:00', '11:00:00', 'Room A', 50, 46, 'AI & Machine Learning', 'Technology'),
('S071', 'Architecting ScaNN for Cost Efficiency', 'Join this session to explore how ScaNN can be used for Cost Efficiency. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Intermediate', 'http://sessions.com/S071', 'Cloud Architect', '2026-04-11', '14:00:00', '14:00:00', 'Auditorium', 100, 97, 'AI & Machine Learning', 'Finance'),
('S072', 'Scaling ScaNN for Analytics', 'Join this session to explore how ScaNN can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S072', 'Cloud Architect', '2026-04-09', '11:00:00', '11:00:00', 'Room C', 100, 96, 'AI & Machine Learning', 'Manufacturing'),
('S073', 'Securing Cloud SQL for Modernization', 'Join this session to explore how Cloud SQL can be used for Modernization. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Advanced', 'http://sessions.com/S073', 'Tech Lead', '2026-04-10', '16:00:00', '16:00:00', 'Room C', 200, 195, 'Security', 'Technology'),
('S074', 'Deploying ScaNN for Scale', 'Join this session to explore how ScaNN can be used for Scale. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S074', 'Data Scientist', '2026-04-10', '10:00:00', '10:00:00', 'Room A', 500, 215, 'AI & Machine Learning', 'Technology'),
('S075', 'Architecting BigQuery for Automation', 'Join this session to explore how BigQuery can be used for Automation. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Advanced', 'http://sessions.com/S075', 'Product Manager', '2026-04-09', '09:00:00', '09:00:00', 'Room A', 200, 110, 'Databases', 'Healthcare'),
('S076', 'Understanding Vertex AI for Scale', 'Join this session to explore how Vertex AI can be used for Scale. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Advanced', 'http://sessions.com/S076', 'Software Engineer', '2026-04-11', '15:00:00', '15:00:00', 'Room B', 200, 15, 'AI & Machine Learning', 'Finance'),
('S077', 'Architecting PostgreSQL for Reliability', 'Join this session to explore how PostgreSQL can be used for Reliability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Advanced', 'http://sessions.com/S077', 'Director of Engineering', '2026-04-09', '11:00:00', '11:00:00', 'Room C', 200, 133, 'Databases', 'Finance'),
('S078', 'Deploying Generative AI for Analytics', 'Join this session to explore how Generative AI can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Intermediate', 'http://sessions.com/S078', 'Product Manager', '2026-04-11', '16:00:00', '16:00:00', 'Room A', 200, 190, 'AI & Machine Learning', 'Manufacturing'),
('S079', 'Deploying ScaNN for Analytics', 'Join this session to explore how ScaNN can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Advanced', 'http://sessions.com/S079', 'Cloud Architect', '2026-04-11', '16:00:00', '16:00:00', 'Room B', 200, 95, 'AI & Machine Learning', 'Technology'),
('S080', 'Securing Spanner for Security', 'Join this session to explore how Spanner can be used for Security. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Intermediate', 'http://sessions.com/S080', 'Tech Lead', '2026-04-11', '14:00:00', '14:00:00', 'Room A', 500, 310, 'Databases', 'Healthcare'),
('S081', 'Optimizing Cloud SQL for Performance', 'Join this session to explore how Cloud SQL can be used for Performance. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Beginner', 'http://sessions.com/S081', 'Security Engineer', '2026-04-11', '11:00:00', '11:00:00', 'Room B', 100, 76, 'Databases', 'Finance'),
('S082', 'Exploring Spanner for Cost Efficiency', 'Join this session to explore how Spanner can be used for Cost Efficiency. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Advanced', 'http://sessions.com/S082', 'Tech Lead', '2026-04-11', '15:00:00', '15:00:00', 'Room C', 50, 1, 'Databases', 'Manufacturing'),
('S083', 'Understanding Kubernetes for Performance', 'Join this session to explore how Kubernetes can be used for Performance. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Beginner', 'http://sessions.com/S083', 'Product Manager', '2026-04-11', '14:00:00', '14:00:00', 'Auditorium', 100, 49, 'Security', 'Retail'),
('S084', 'Exploring Cloud SQL for Cost Efficiency', 'Join this session to explore how Cloud SQL can be used for Cost Efficiency. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Intermediate', 'http://sessions.com/S084', 'Tech Lead', '2026-04-09', '16:00:00', '16:00:00', 'Room C', 500, 91, 'Cloud Architecture', 'Healthcare'),
('S085', 'Scaling Kubernetes for Automation', 'Join this session to explore how Kubernetes can be used for Automation. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Intermediate', 'http://sessions.com/S085', 'Director of Engineering', '2026-04-10', '10:00:00', '10:00:00', 'Room B', 500, 204, 'Databases', 'Finance'),
('S086', 'Deep Dive into BigQuery for Cost Efficiency', 'Join this session to explore how BigQuery can be used for Cost Efficiency. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S086', 'Product Manager', '2026-04-09', '16:00:00', '16:00:00', 'Room B', 100, 89, 'Databases', 'Finance'),
('S087', 'Deep Dive into Vertex AI for Automation', 'Join this session to explore how Vertex AI can be used for Automation. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S087', 'Data Scientist', '2026-04-10', '14:00:00', '14:00:00', 'Auditorium', 100, 74, 'AI & Machine Learning', 'Retail'),
('S088', 'Mastering AlloyDB for Modernization', 'Join this session to explore how AlloyDB can be used for Modernization. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S088', 'Security Engineer', '2026-04-09', '16:00:00', '16:00:00', 'Room C', 100, 39, 'Databases', 'Healthcare'),
('S089', 'Exploring AlloyDB for High Availability', 'Join this session to explore how AlloyDB can be used for High Availability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Advanced', 'http://sessions.com/S089', 'Data Scientist', '2026-04-10', '15:00:00', '15:00:00', 'Room A', 100, 94, 'Databases', 'Healthcare'),
('S090', 'Exploring Vertex AI for Security', 'Join this session to explore how Vertex AI can be used for Security. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Intermediate', 'http://sessions.com/S090', 'Security Engineer', '2026-04-11', '10:00:00', '10:00:00', 'Room B', 50, 27, 'AI & Machine Learning', 'Retail'),
('S091', 'Mastering ScaNN for Cost Efficiency', 'Join this session to explore how ScaNN can be used for Cost Efficiency. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Advanced', 'http://sessions.com/S091', 'DevOps Engineer', '2026-04-09', '14:00:00', '14:00:00', 'Auditorium', 100, 21, 'AI & Machine Learning', 'Technology'),
('S092', 'Mastering BigQuery for Scale', 'Join this session to explore how BigQuery can be used for Scale. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Advanced', 'http://sessions.com/S092', 'Cloud Architect', '2026-04-09', '16:00:00', '16:00:00', 'Room C', 200, 67, 'Databases', 'Manufacturing'),
('S093', 'Exploring AlloyDB for Performance', 'Join this session to explore how AlloyDB can be used for Performance. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S093', 'Security Engineer', '2026-04-09', '10:00:00', '10:00:00', 'Room B', 200, 171, 'Databases', 'Finance'),
('S094', 'Deploying Cloud SQL for Modernization', 'Join this session to explore how Cloud SQL can be used for Modernization. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Advanced', 'http://sessions.com/S094', 'Product Manager', '2026-04-09', '14:00:00', '14:00:00', 'Room B', 100, 71, 'DevOps', 'Healthcare'),
('S095', 'Building Spanner for Analytics', 'Join this session to explore how Spanner can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S095', 'Product Manager', '2026-04-10', '15:00:00', '15:00:00', 'Room B', 500, 334, 'Databases', 'Manufacturing'),
('S096', 'Deep Dive into Vertex AI for Analytics', 'Join this session to explore how Vertex AI can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Intermediate', 'http://sessions.com/S096', 'DevOps Engineer', '2026-04-11', '14:00:00', '14:00:00', 'Room C', 100, 27, 'AI & Machine Learning', 'Healthcare'),
('S097', 'Deploying PostgreSQL for Analytics', 'Join this session to explore how PostgreSQL can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Breakouts', 'Beginner', 'http://sessions.com/S097', 'Cloud Architect', '2026-04-09', '11:00:00', '11:00:00', 'Room C', 50, 36, 'Databases', 'Healthcare'),
('S098', 'Architecting Kubernetes for Reliability', 'Join this session to explore how Kubernetes can be used for Reliability. We will cover best practices, real-world use cases, and advanced configuration options.', 'Lightning Talks', 'Beginner', 'http://sessions.com/S098', 'Cloud Architect', '2026-04-10', '11:00:00', '11:00:00', 'Room A', 50, 29, 'AI & Machine Learning', 'Finance'),
('S099', 'Scaling Cloud SQL for Analytics', 'Join this session to explore how Cloud SQL can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Beginner', 'http://sessions.com/S099', 'DevOps Engineer', '2026-04-09', '09:00:00', '09:00:00', 'Auditorium', 50, 21, 'Security', 'Finance'),
('S100', 'Deploying PostgreSQL for Analytics', 'Join this session to explore how PostgreSQL can be used for Analytics. We will cover best practices, real-world use cases, and advanced configuration options.', 'Keynotes', 'Advanced', 'http://sessions.com/S100', 'DevOps Engineer', '2026-04-11', '09:00:00', '09:00:00', 'Room B', 500, 141, 'Databases', 'Technology') ON CONFLICT (session_id) DO NOTHING;

3.5 세션에 발표자 할당

-- Insert Session-Speaker Mappings
INSERT INTO public.session_speaker_mapping (session_id, speaker_id) VALUES
('S001', 6),
('S001', 19),
('S002', 6),
('S002', 11),
('S003', 14),
('S003', 10),
('S004', 5),
('S004', 8),
('S005', 12),
('S005', 9),
('S006', 14),
('S007', 14),
('S007', 3),
('S008', 2),
('S008', 4),
('S009', 9),
('S009', 13),
('S010', 2),
('S010', 3),
('S011', 12),
('S012', 13),
('S013', 11),
('S014', 8),
('S015', 16),
('S016', 4),
('S016', 18),
('S017', 12),
('S018', 10),
('S019', 15),
('S020', 20),
('S020', 7),
('S021', 2),
('S021', 11),
('S022', 15),
('S022', 7),
('S023', 4),
('S023', 19),
('S024', 11),
('S025', 11),
('S025', 10),
('S026', 11),
('S027', 20),
('S028', 20),
('S028', 3),
('S029', 11),
('S029', 20),
('S030', 19),
('S030', 6),
('S031', 20),
('S031', 14),
('S032', 3),
('S033', 4),
('S033', 8),
('S034', 14),
('S035', 12),
('S035', 4),
('S036', 18),
('S037', 6),
('S038', 9),
('S038', 7),
('S039', 7),
('S040', 17),
('S041', 17),
('S042', 16),
('S042', 2),
('S043', 10),
('S043', 13),
('S044', 7),
('S045', 10),
('S046', 1),
('S046', 20),
('S047', 17),
('S048', 9),
('S048', 6),
('S049', 4),
('S049', 3),
('S050', 8),
('S050', 16),
('S051', 18),
('S051', 10),
('S052', 4),
('S053', 5),
('S053', 13),
('S054', 17),
('S055', 1),
('S056', 1),
('S056', 15),
('S057', 7),
('S058', 15),
('S059', 15),
('S059', 8),
('S060', 20),
('S060', 10),
('S061', 16),
('S062', 18),
('S062', 11),
('S063', 10),
('S064', 6),
('S065', 10),
('S066', 8),
('S066', 9),
('S067', 16),
('S067', 14),
('S068', 13),
('S068', 11),
('S069', 16),
('S070', 8),
('S071', 3),
('S072', 10),
('S072', 1),
('S073', 10),
('S073', 8),
('S074', 20),
('S074', 14),
('S075', 5),
('S076', 1),
('S076', 11),
('S077', 3),
('S078', 20),
('S078', 12),
('S079', 17),
('S079', 9),
('S080', 10),
('S081', 2),
('S082', 15),
('S082', 11),
('S083', 7),
('S083', 1),
('S084', 8),
('S085', 8),
('S085', 18),
('S086', 5),
('S087', 5),
('S088', 19),
('S088', 18),
('S089', 18),
('S090', 8),
('S091', 4),
('S092', 14),
('S093', 20),
('S093', 17),
('S094', 16),
('S094', 9),
('S095', 11),
('S095', 4),
('S096', 14),
('S096', 11),
('S097', 13),
('S097', 19),
('S098', 3),
('S099', 11),
('S099', 18),
('S100', 18),
('S100', 13) ON CONFLICT DO NOTHING;

3.6 세션에 주제 첨부

-- Insert Session-Topic Mappings
INSERT INTO public.session_topic_mapping (session_id, topic) VALUES
('S001', 'Security'),
('S002', 'AI & Machine Learning'),
('S003', 'Cloud Architecture'),
('S003', 'AI & Machine Learning'),
('S004', 'Databases'),
('S004', 'DevOps'),
('S005', 'DevOps'),
('S005', 'Cloud Architecture'),
('S006', 'Security'),
('S006', 'AI & Machine Learning'),
('S007', 'Cloud Architecture'),
('S007', 'Databases'),
('S008', 'AI & Machine Learning'),
('S008', 'Databases'),
('S009', 'Security'),
('S009', 'DevOps'),
('S010', 'AI & Machine Learning'),
('S010', 'Cloud Architecture'),
('S011', 'Security'),
('S012', 'Databases'),
('S013', 'Cloud Architecture'),
('S014', 'Databases'),
('S015', 'DevOps'),
('S015', 'Security'),
('S016', 'AI & Machine Learning'),
('S016', 'Databases'),
('S017', 'Cloud Architecture'),
('S018', 'AI & Machine Learning'),
('S019', 'AI & Machine Learning'),
('S020', 'Security'),
('S021', 'Security'),
('S021', 'Databases'),
('S022', 'Security'),
('S022', 'Databases'),
('S023', 'DevOps'),
('S023', 'AI & Machine Learning'),
('S024', 'AI & Machine Learning'),
('S025', 'Databases'),
('S026', 'DevOps'),
('S027', 'DevOps'),
('S027', 'Cloud Architecture'),
('S028', 'DevOps'),
('S029', 'Databases'),
('S029', 'Security'),
('S030', 'DevOps'),
('S030', 'Security'),
('S031', 'Databases'),
('S032', 'Databases'),
('S033', 'Databases'),
('S034', 'AI & Machine Learning'),
('S035', 'Security'),
('S035', 'AI & Machine Learning'),
('S036', 'AI & Machine Learning'),
('S036', 'Databases'),
('S037', 'DevOps'),
('S037', 'Databases'),
('S038', 'Cloud Architecture'),
('S038', 'Security'),
('S039', 'Security'),
('S040', 'Security'),
('S040', 'AI & Machine Learning'),
('S041', 'AI & Machine Learning'),
('S042', 'Databases'),
('S043', 'AI & Machine Learning'),
('S043', 'Databases'),
('S044', 'Databases'),
('S044', 'DevOps'),
('S045', 'DevOps'),
('S045', 'Cloud Architecture'),
('S046', 'Cloud Architecture'),
('S046', 'Security'),
('S047', 'Cloud Architecture'),
('S047', 'Security'),
('S048', 'Databases'),
('S049', 'AI & Machine Learning'),
('S049', 'Databases'),
('S050', 'Cloud Architecture'),
('S050', 'AI & Machine Learning'),
('S051', 'DevOps'),
('S051', 'Databases'),
('S052', 'Databases'),
('S053', 'DevOps'),
('S053', 'Databases'),
('S054', 'Security'),
('S054', 'DevOps'),
('S055', 'AI & Machine Learning'),
('S056', 'Security'),
('S056', 'DevOps'),
('S057', 'AI & Machine Learning'),
('S058', 'Security'),
('S058', 'AI & Machine Learning'),
('S059', 'Security'),
('S060', 'AI & Machine Learning'),
('S060', 'DevOps'),
('S061', 'Security'),
('S061', 'AI & Machine Learning'),
('S062', 'DevOps'),
('S063', 'Security'),
('S063', 'DevOps'),
('S064', 'Databases'),
('S064', 'AI & Machine Learning'),
('S065', 'DevOps'),
('S066', 'AI & Machine Learning'),
('S066', 'Cloud Architecture'),
('S067', 'AI & Machine Learning'),
('S068', 'Security'),
('S069', 'Cloud Architecture'),
('S069', 'DevOps'),
('S070', 'AI & Machine Learning'),
('S071', 'AI & Machine Learning'),
('S071', 'DevOps'),
('S072', 'DevOps'),
('S072', 'Security'),
('S073', 'Security'),
('S074', 'AI & Machine Learning'),
('S074', 'Databases'),
('S075', 'AI & Machine Learning'),
('S076', 'DevOps'),
('S077', 'DevOps'),
('S077', 'AI & Machine Learning'),
('S078', 'Databases'),
('S079', 'Databases'),
('S080', 'AI & Machine Learning'),
('S080', 'Databases'),
('S081', 'Databases'),
('S082', 'Databases'),
('S083', 'Security'),
('S084', 'Security'),
('S084', 'AI & Machine Learning'),
('S085', 'AI & Machine Learning'),
('S085', 'Databases'),
('S086', 'DevOps'),
('S087', 'Databases'),
('S088', 'Security'),
('S088', 'Databases'),
('S089', 'Databases'),
('S090', 'DevOps'),
('S091', 'Databases'),
('S092', 'Databases'),
('S093', 'Security'),
('S093', 'AI & Machine Learning'),
('S094', 'Cloud Architecture'),
('S095', 'Cloud Architecture'),
('S095', 'AI & Machine Learning'),
('S096', 'AI & Machine Learning'),
('S097', 'Security'),
('S097', 'AI & Machine Learning'),
('S098', 'Databases'),
('S098', 'Security'),
('S099', 'Cloud Architecture'),
('S100', 'DevOps'),
('S100', 'Cloud Architecture') ON CONFLICT DO NOTHING;;

3.7 참석자를 세션에 할당하기

-- Insert Attendee-Session Mappings
INSERT INTO public.attendees_sessions (username, session_id) VALUES
('user_1', 'S100'),
('user_1', 'S008'),
('user_1', 'S060'),
('user_1', 'S090'),
('user_1', 'S057'),
('user_2', 'S086'),
('user_2', 'S033'),
('user_2', 'S006'),
('user_2', 'S043'),
('user_2', 'S050'),
('user_3', 'S066'),
('user_3', 'S099'),
('user_4', 'S004'),
('user_4', 'S043'),
('user_4', 'S092'),
('user_4', 'S033'),
('user_4', 'S074'),
('user_5', 'S014'),
('user_5', 'S088'),
('user_5', 'S093'),
('user_6', 'S075'),
('user_6', 'S033'),
('user_7', 'S014'),
('user_7', 'S021'),
('user_7', 'S047'),
('user_8', 'S051'),
('user_8', 'S081'),
('user_9', 'S048'),
('user_9', 'S100'),
('user_10', 'S037'),
('user_10', 'S059'),
('user_10', 'S083'),
('user_11', 'S007'),
('user_11', 'S099'),
('user_11', 'S054'),
('user_12', 'S006'),
('user_12', 'S046'),
('user_12', 'S077'),
('user_12', 'S032'),
('user_13', 'S020'),
('user_13', 'S029'),
('user_13', 'S054'),
('user_14', 'S052'),
('user_14', 'S070'),
('user_14', 'S028'),
('user_15', 'S054'),
('user_15', 'S050'),
('user_15', 'S025'),
('user_15', 'S066'),
('user_15', 'S081'),
('user_16', 'S099'),
('user_16', 'S073'),
('user_16', 'S027'),
('user_16', 'S058'),
('user_17', 'S092'),
('user_17', 'S089'),
('user_17', 'S076'),
('user_17', 'S062'),
('user_18', 'S083'),
('user_18', 'S094'),
('user_18', 'S097'),
('user_18', 'S031'),
('user_18', 'S040'),
('user_19', 'S078'),
('user_19', 'S072'),
('user_19', 'S049'),
('user_19', 'S017'),
('user_19', 'S084'),
('user_20', 'S023'),
('user_20', 'S003'),
('user_20', 'S016'),
('user_20', 'S068'),
('user_21', 'S071'),
('user_21', 'S058'),
('user_21', 'S043'),
('user_21', 'S079'),
('user_21', 'S067'),
('user_22', 'S076'),
('user_22', 'S038'),
('user_22', 'S049'),
('user_22', 'S033'),
('user_22', 'S070'),
('user_23', 'S032'),
('user_23', 'S099'),
('user_24', 'S022'),
('user_24', 'S065'),
('user_24', 'S060'),
('user_24', 'S084'),
('user_25', 'S077'),
('user_25', 'S080'),
('user_25', 'S097'),
('user_25', 'S010'),
('user_26', 'S063'),
('user_26', 'S052'),
('user_26', 'S086'),
('user_27', 'S054'),
('user_27', 'S094'),
('user_27', 'S018'),
('user_27', 'S061'),
('user_27', 'S052'),
('user_28', 'S064'),
('user_28', 'S070'),
('user_28', 'S050'),
('user_29', 'S009'),
('user_29', 'S012'),
('user_30', 'S058'),
('user_30', 'S056'),
('user_30', 'S072'),
('user_30', 'S093'),
('user_30', 'S045'),
('user_31', 'S001'),
('user_31', 'S094'),
('user_31', 'S065'),
('user_31', 'S031'),
('user_32', 'S048'),
('user_32', 'S011'),
('user_32', 'S065'),
('user_33', 'S021'),
('user_33', 'S081'),
('user_33', 'S063'),
('user_34', 'S068'),
('user_34', 'S026'),
('user_35', 'S044'),
('user_35', 'S054'),
('user_36', 'S023'),
('user_36', 'S051'),
('user_36', 'S100'),
('user_37', 'S047'),
('user_37', 'S053'),
('user_37', 'S057'),
('user_37', 'S048'),
('user_37', 'S080'),
('user_38', 'S003'),
('user_38', 'S038'),
('user_38', 'S046'),
('user_38', 'S005'),
('user_38', 'S076'),
('user_39', 'S046'),
('user_39', 'S020'),
('user_39', 'S043'),
('user_39', 'S002'),
('user_39', 'S100'),
('user_40', 'S019'),
('user_40', 'S098'),
('user_40', 'S053'),
('user_40', 'S007'),
('user_41', 'S100'),
('user_41', 'S032'),
('user_41', 'S048'),
('user_41', 'S064'),
('user_42', 'S086'),
('user_42', 'S030'),
('user_42', 'S049'),
('user_43', 'S033'),
('user_43', 'S008'),
('user_43', 'S049'),
('user_43', 'S093'),
('user_44', 'S078'),
('user_44', 'S071'),
('user_44', 'S067'),
('user_44', 'S012'),
('user_45', 'S043'),
('user_45', 'S056'),
('user_45', 'S079'),
('user_45', 'S062'),
('user_45', 'S039'),
('user_46', 'S054'),
('user_46', 'S031'),
('user_46', 'S018'),
('user_47', 'S035'),
('user_47', 'S100'),
('user_47', 'S083'),
('user_47', 'S080'),
('user_47', 'S037'),
('user_48', 'S097'),
('user_48', 'S026'),
('user_48', 'S065'),
('user_48', 'S030'),
('user_48', 'S074'),
('user_49', 'S022'),
('user_49', 'S089'),
('user_49', 'S038'),
('user_49', 'S047'),
('user_49', 'S073'),
('user_50', 'S074'),
('user_50', 'S033'),
('user_50', 'S069'),
('user_50', 'S036') ON CONFLICT DO NOTHING;

성공적인 실행을 나타내는 출력이 표시됩니다. 테이블이 채워져 있는지 확인합니다. 예를 들어 세션 테이블에 세션이 100개 있는지 확인합니다.

SELECT count(*) from public.session;

7. AI 쿼리 엔진 사용 설정

AI 기능을 사용하려면 데이터베이스에서 AI 쿼리 엔진을 사용 설정해야 합니다.

AlloyDB Studio (conference_db 컨텍스트)에서 다음 SQL을 실행합니다.

CREATE EXTENSION IF NOT EXISTS google_ml_integration CASCADE;
ALTER DATABASE conference_db SET google_ml_integration.enable_ai_query_engine = 'on';

확장 프로그램이 사용 설정되어 있는지 확인합니다.

SELECT extversion FROM pg_extension WHERE extname = 'google_ml_integration';

예상 출력은 1.5.9 이상이어야 합니다.

8. AI 함수 (연산자) 사용

이제 ai.if 및 ai.rank 함수를 사용하여 AI 기반 시맨틱 필터링 및 점수 매기기를 사용해 보겠습니다.

ai.if를 사용한 시맨틱 필터링

정확한 키워드가 없으면 표준 텍스트 일치에서 세션이 누락될 수 있습니다. ai.if를 사용하여 '생성형 AI'에 관한 세션을 찾아보자.

AlloyDB Studio에서 다음 SQL을 실행합니다.

SELECT session_name, full_description
FROM public.session
WHERE
  ai.if(
    prompt => 'Is this session description about Generative AI, LLMs or AI agents? ' || full_description
  );

다른 용어를 사용하더라도 관련 세션이 결과에 표시됩니다.

ai.rank를 사용한 시맨틱 점수 매기기

초보자 친화도에 따라 세션에 점수를 매겨 보겠습니다. 의미론적 점수 매기기에 ai.rank을 사용할 수 있습니다.

다음 SQL을 실행합니다.

SELECT session_name, full_description, 
  ai.rank(
    prompt => 'On a scale of 0 to 1, how suitable is this session for a beginner? 1 being very suitable, 0 being advanced. Session: ' || full_description
  ) as beginner_friendly_score
FROM public.session
ORDER BY beginner_friendly_score DESC;

이는 프로필을 기반으로 회의 참석자에게 맞춤 추천을 제공하는 데 유용합니다.

9. 하이브리드 검색 구현

하이브리드 검색은 키워드 검색 (어휘)의 정확성과 벡터 검색 (시맨틱)의 컨텍스트 이해를 결합합니다. 두 가지 모두에 대한 색인을 만들고 상호 순위 융합 (RRF)을 사용하여 결과를 병합합니다.

스캔 사용 설정

scann 및 rum 확장 프로그램이 사용 설정되어 있는지 확인합니다.

CREATE EXTENSION IF NOT EXISTS alloydb_scann;
CREATE EXTENSION IF NOT EXISTS rum;

색인 만들기

스키마에서 임베딩 (full_description_embedding)에 생성된 열을 사용하므로 삽입 시 자동으로 계산됩니다. 빠른 검색을 위해 적절한 색인을 만들기만 하면 됩니다.

전체 텍스트 키워드 검색을 위한 RUM 색인 (FTS 색인)과 빠른 벡터 유사성 검색을 위한 ScaNN 색인을 만듭니다.

AlloyDB Studio에서 다음 SQL을 실행합니다.

-- Create RUM index for text search on session descriptions
CREATE INDEX session_text_idx ON public.session USING RUM (description_tsvector rum_tsvector_ops);

-- Create ScaNN index for vector search on session embeddings
CREATE INDEX session_vector_idx ON public.session
  USING scann (full_description_embedding cosine)
  WITH (num_leaves=10);

RRF를 사용하여 하이브리드 검색 실행

이제 fast similarity search의 세션을 찾되 AlloyDB의 ScaNN에 집중하려고 합니다.

시맨틱 검색과 키워드 검색을 사용하고 RRF를 사용하여 결과를 병합합니다.

다음 SQL을 실행합니다.

-- Enable preview features for the AI Query Engine if not already set
SET google_ml_integration.enable_preview_ai_functions = true;
SELECT s.session_id, s.session_name, s.full_description
FROM public.session s
JOIN ai.hybrid_search(
  search_inputs => ARRAY[
      '{
        "data_type": "vector",
        "table_name": "session",
        "key_column": "session_id",
        "vec_column": "full_description_embedding",
        "distance_operator": "public.<=>",
        "limit": 5,
        "query_vector": "ai.embedding(''text-embedding-005'', ''fast similarity search'')::vector"
      }'::JSONB,
      '{
        "data_type": "text",
        "table_name": "session",
        "key_column": "session_id",
        "text_column": "description_tsvector",
        "limit": 5,
        "ranking_function": "<=>",
        "query_text_input": "ScaNN"
      }'::JSONB
  ]
) AS search_results
on s.session_id = search_results.id;

관련 세션 유사성 검색이 표시되고 대부분의 결과에 ScaNN 관련 세션이 표시됩니다.

10. AlloyDB Studio에서 QueryData 사용

AlloyDB AI를 사용하면 QueryData를 사용하여 자연어 입력에서 정확하고 예측 가능한 SQL 문을 생성할 수 있습니다. 이 섹션에서는 QueryData 컨텍스트 (템플릿)를 만들고 AlloyDB Studio에서 직접 테스트하는 방법을 알아봅니다.

QueryData 컨텍스트 만들기

QueryData 컨텍스트는 AI 모델이 올바른 SQL 쿼리 또는 SQL 쿼리 부분을 사용하는 데 필요한 데이터와 방향을 제공하는 쿼리 템플릿과 패싯이 포함된 JSON 파일입니다.

회의 스키마를 위해 설계된 샘플 JSON 컨텍스트를 살펴보겠습니다. 표 이름이 스키마 (예: public.session)와 일치하도록 합니다.

다음은 사용할 JSON 콘텐츠입니다. 노트북/컴퓨터의 로컬 .json 파일에 저장하세요.

{
  "templates": [
    {
      "nlQuery": "Advanced sessions that are almost full",
      "sql": "SELECT session_name, remaining_capacity, session_date, session_start_time FROM session WHERE learning_level = 'Advanced' AND remaining_capacity > 0 AND remaining_capacity < 5 ORDER BY remaining_capacity ASC",
      "intent": "Advanced sessions that are almost full (remaining capacity less than 5)",
      "manifest": "Advanced sessions that are almost full (remaining capacity less than 5)",
      "parameterized": {
        "parameterized_intent": "$1 sessions that are almost full (remaining capacity less than 5)",
        "parameterized_sql": "SELECT session_name, remaining_capacity, session_date, session_start_time FROM public.session WHERE learning_level = $1 AND remaining_capacity > 0 AND remaining_capacity < 5 ORDER BY remaining_capacity ASC"
      }
    },
    {
      "nlQuery": "Find sessions about Gemini",
      "sql": "SELECT name, format, session_date, description FROM ((SELECT s1.session_name AS name, s1.session_format AS format, s1.session_date AS session_date, s1.full_description AS description, (s1.full_description_embedding <=> public.embedding('text-embedding-005', 'Gemini')::public.vector) AS distance FROM public.session s1 ORDER BY distance LIMIT 10) UNION ALL (SELECT s2.session_name AS name, s2.session_format AS format, s2.session_date AS session_date, t.topic_desc AS description, (t.embedding <=> public.embedding('text-embedding-005', 'Gemini')::public.vector) AS distance FROM public.session s2 INNER JOIN public.session_topic_mapping stm ON s2.session_id = stm.session_id INNER JOIN public.session_topic t ON stm.topic = t.topic ORDER BY distance LIMIT 10)) AS combined_results ORDER BY distance LIMIT 10",
      "intent": "Find sessions about Gemini",
      "manifest": "Find sessions about a given topic",
      "parameterized": {
        "parameterized_intent": "Find sessions about $1",
        "parameterized_sql": "SELECT name, format, session_date, description FROM ((SELECT s1.session_name AS name, s1.session_format AS format, s1.session_date AS session_date, s1.full_description AS description, (s1.full_description_embedding <=> public.embedding('text-embedding-005', '$1')::public.vector) AS distance FROM public.session s1 ORDER BY distance LIMIT 10) UNION ALL (SELECT s2.session_name AS name, s2.session_format AS format, s2.session_date AS session_date, t.topic_desc AS description, (t.embedding <=> public.embedding('text-embedding-005', '$1')::public.vector) AS distance FROM public.session s2 INNER JOIN public.session_topic_mapping stm ON s2.session_id = stm.session_id INNER JOIN public.session_topic t ON stm.topic = t.topic ORDER BY distance LIMIT 10)) AS combined_results ORDER BY distance LIMIT 10"
      }
    }
  ]
}

AlloyDB Studio에서 QueryData 컨텍스트 로드

'데이터 쿼리' 컨텍스트를 사용하려면 AlloyDB Studio를 통해 데이터베이스에 업로드해야 합니다.

Google Cloud 콘솔에서 AlloyDB Studio를 엽니다.
하단의 왼쪽 패널에 컨텍스트 세트와 점 3개가 표시됩니다.
이 버튼을 클릭하고 컨텍스트 세트 만들기를 선택합니다.
대화상자를 작성합니다.
- 이름: conference_context
- 설명: Conference Sessions QueryData Context
- 컨텍스트 파일 업로드: 위의 콘텐츠로 만든 JSON 파일을 업로드합니다.
저장

QueryData 컨텍스트 테스트

업로드한 후 AlloyDB Studio에서 직접 테스트할 수 있습니다.

방금 만든 컨텍스트 옆에 있는 점 3개를 클릭하고 컨텍스트 세트 테스트를 선택합니다 (또는 쿼리 편집기에서 Gemini 필을 사용하고 이 컨텍스트를 선택합니다).
Gemini SQL 생성 프롬프트에 템플릿과 일치하는 자연어 쿼리를 입력합니다.
```
Advanced sessions that are almost full
```
SQL을 생성하고 제공된 템플릿과 일치하는지 확인합니다.
자연어 프롬프트에서 '고급'을 '초급'으로 변경하여 매개변수화된 질문을 시도하고 적응하는지 확인해 보세요.

11. 삭제

AlloyDB 클러스터 삭제

Google Cloud 계정에 지속적으로 비용이 청구되지 않도록 하려면 이 Codelab 중에 만든 리소스를 삭제하세요.

Cloud Shell에서 다음 명령어를 실행하여 AlloyDB 클러스터를 삭제합니다 (인스턴스도 삭제됨).

export REGION=us-central1
export ADBCLUSTER=alloydb-next26-ai-demo-01

echo "=> Deleting AlloyDB Cluster (${ADBCLUSTER})..."
gcloud alloydb clusters delete $ADBCLUSTER --region=$REGION --force

또는 이 Codelab용으로 프로젝트를 만든 경우 전체 프로젝트를 삭제해도 됩니다.

IP 범위 및 vpc-peering 삭제

PROJECT_ID=$(gcloud config get-value project)
echo "=> Deleting Service Networking VPC Peering..."
gcloud compute networks peerings delete servicenetworking-googleapis-com \
    --network=default \
    --project=${PROJECT_ID} --quiet || true

echo "=> Deleting Allocated IP Range for Managed Services..."
gcloud compute addresses delete psa-range \
    --global \
    --project=${PROJECT_ID} --quiet || true

로컬 파일 삭제

Work with QueryData in AlloyDB Studio 단계에서 만든 로컬 JSON 파일을 삭제합니다.

12. 축하합니다

축하합니다. 통합된 컨퍼런스 세션 시나리오를 사용하여 여러 AlloyDB AI 기능을 살펴봤습니다.

학습한 내용

시맨틱 필터링 및 점수 매기기를 위해 AI 함수 (ai.if, ai.rank)를 사용 설정하고 사용하는 방법
상호 순위 융합 (RRF)을 사용하여 ScaNN (벡터) 및 RUM (텍스트) 색인으로 하이브리드 검색을 구현하는 방법
AlloyDB Studio에서 QueryData를 사용하여 자연어에서 예측 가능한 SQL을 생성하는 방법

다음 단계

AlloyDB AI 문서를 살펴봅니다.
pgvector 및 ScaNN에 대해 자세히 알아보세요.