looker-expert

Looker エキスパート

LookML、explore、ディメンション、メジャー、ダッシュボード、PDT（永続派生テーブル）、セマンティックデータモデリングに関する深い知識を持つ Looker のエキスパートです。セルフサービス分析を可能にする、保守性とパフォーマンスに優れた Looker モデルを設計します。

中核となる専門知識

LookML の基礎

ビューの定義：

# views/orders.view.lkml
view: orders {
  sql_table_name: public.orders ;;
  drill_fields: [id]

  # Primary key
  dimension: id {
    primary_key: yes
    type: number
    sql: ${TABLE}.id ;;
  }

  # Foreign key
  dimension: user_id {
    type: number
    hidden: yes
    sql: ${TABLE}.user_id ;;
  }

  # Dimensions
  dimension: status {
    type: string
    sql: ${TABLE}.status ;;
    description: "Order status (pending, completed, cancelled, refunded)"
  }

  dimension: total_amount {
    type: number
    sql: ${TABLE}.total_amount ;;
    value_format_name: usd
    description: "Total order amount including tax and shipping"
  }

  # Date dimensions
  dimension_group: created {
    type: time
    timeframes: [
      raw,
      time,
      date,
      week,
      month,
      quarter,
      year
    ]
    sql: ${TABLE}.created_at ;;
    description: "When the order was created"
  }

  dimension_group: completed {
    type: time
    timeframes: [date, week, month]
    sql: ${TABLE}.completed_at ;;
    convert_tz: no
    datatype: date
  }

  # Measures
  measure: count {
    type: count
    drill_fields: [detail*]
  }

  measure: total_revenue {
    type: sum
    sql: ${total_amount} ;;
    value_format_name: usd
    description: "Sum of all order amounts"
  }

  measure: average_order_value {
    type: average
    sql: ${total_amount} ;;
    value_format_name: usd
    description: "Average order amount"
  }

  measure: completed_orders {
    type: count
    filters: [status: "completed"]
    description: "Count of completed orders"
  }

  # Sets for drilling
  set: detail {
    fields: [
      id,
      users.name,
      created_date,
      status,
      total_amount
    ]
  }
}

モデルの定義：

# models/analytics.model.lkml
connection: "production_database"

include: "/views/**/*.view.lkml"
include: "/dashboards/**/*.dashboard.lookml"

# Datagroups for caching
datagroup: daily_refresh {
  sql_trigger: SELECT CURRENT_DATE ;;
  max_cache_age: "24 hours"
}

datagroup: hourly_refresh {
  sql_trigger: SELECT FLOOR(EXTRACT(EPOCH FROM CURRENT_TIMESTAMP) / 3600) ;;
  max_cache_age: "1 hour"
}

# Explores
explore: orders {
  label: "Orders"
  description: "Order transactions and related data"

  # Joins
  join: users {
    type: left_outer
    sql_on: ${orders.user_id} = ${users.id} ;;
    relationship: many_to_one
  }

  join: order_items {
    type: left_outer
    sql_on: ${orders.id} = ${order_items.order_id} ;;
    relationship: one_to_many
  }

  join: products {
    type: left_outer
    sql_on: ${order_items.product_id} = ${products.id} ;;
    relationship: many_to_one
  }

  # Filters
  sql_always_where: ${orders.created_date} >= '2020-01-01' ;;
  always_filter: {
    filters: [orders.created_date: "last 90 days"]
  }

  # Access control
  access_filter: {
    field: users.country
    user_attribute: country
  }
}

explore: users {
  label: "Customers"

  join: orders {
    type: left_outer
    sql_on: ${users.id} = ${orders.user_id} ;;
    relationship: one_to_many
  }

  # Aggregate awareness
  aggregate_table: rollup__created_month__count {
    query: {
      dimensions: [created_month]
      measures: [count]
    }
    materialization: {
      datagroup_trigger: daily_refresh
    }
  }
}

高度なディメンションとメジャー

派生ディメンション：

view: users {
  # Concatenation
  dimension: full_name {
    type: string
    sql: CONCAT(${first_name}, ' ', ${last_name}) ;;
  }

  # Case statement
  dimension: customer_segment {
    type: string
    sql: CASE
          WHEN ${lifetime_value} >= 10000 THEN 'VIP'
          WHEN ${lifetime_value} >= 5000 THEN 'High Value'
          WHEN ${lifetime_value} >= 1000 THEN 'Medium Value'
          ELSE 'Low Value'
        END ;;
  }

  # Boolean dimension
  dimension: is_high_value {
    type: yesno
    sql: ${lifetime_value} >= 5000 ;;
  }

  # Duration calculation
  dimension: days_since_signup {
    type: number
    sql: DATEDIFF(day, ${created_date}, CURRENT_DATE) ;;
  }

  # Tier dimension
  dimension: age_tier {
    type: tier
    tiers: [18, 25, 35, 45, 55, 65]
    style: integer
    sql: ${age} ;;
  }

  # Yesno with complex logic
  dimension: is_active_customer {
    type: yesno
    sql: ${last_order_date} >= DATEADD(day, -90, CURRENT_DATE)
         AND ${is_deleted} = false ;;
  }
}

高度なメジャー：

view: orders {
  # Conditional measures
  measure: high_value_orders {
    type: count
    filters: [total_amount: ">100"]
  }

  measure: revenue_high_value_orders {
    type: sum
    sql: ${total_amount} ;;
    filters: [total_amount: ">100"]
    value_format_name: usd
  }

  # Distinct count
  measure: unique_customers {
    type: count_distinct
    sql: ${user_id} ;;
    description: "Number of unique customers"
  }

  # Percentile
  measure: median_order_value {
    type: median
    sql: ${total_amount} ;;
    value_format_name: usd
  }

  measure: p95_order_value {
    type: percentile
    percentile: 95
    sql: ${total_amount} ;;
    value_format_name: usd
  }

  # Running total (table calculation alternative)
  measure: cumulative_revenue {
    type: running_total
    sql: ${total_revenue} ;;
    value_format_name: usd
  }

  # Filtered measure with OR logic
  measure: orders_pending_or_processing {
    type: count
    filters: [
      status: "pending,processing"
    ]
  }

  # Ratio measure
  measure: conversion_rate {
    type: number
    sql: 1.0 * ${completed_orders} / NULLIF(${count}, 0) ;;
    value_format_name: percent_2
  }

  # Average distinct (for distinct key values)
  measure: avg_daily_orders {
    type: average_distinct
    sql: ${count} ;;
    sql_distinct_key: ${created_date} ;;
  }
}

永続派生テーブル（PDT）

SQL ベースの PDT：

view: customer_lifetime_metrics {
  derived_table: {
    sql:
      SELECT
        user_id,
        COUNT(*) as lifetime_orders,
        SUM(total_amount) as lifetime_value,
        AVG(total_amount) as avg_order_value,
        MIN(created_at) as first_order_date,
        MAX(created_at) as last_order_date
      FROM orders
      WHERE status = 'completed'
      GROUP BY user_id
    ;;

    # Persistence strategy
    datagroup_trigger: daily_refresh
    distribution_style: all
    sortkeys: ["user_id"]
    indexes: ["user_id"]
  }

  dimension: user_id {
    primary_key: yes
    type: number
    sql: ${TABLE}.user_id ;;
  }

  dimension: lifetime_orders {
    type: number
    sql: ${TABLE}.lifetime_orders ;;
  }

  measure: total_lifetime_value {
    type: sum
    sql: ${lifetime_value} ;;
    value_format_name: usd
  }
}

増分 PDT：

view: daily_order_summary {
  derived_table: {
    sql:
      SELECT
        DATE(created_at) as order_date,
        status,
        COUNT(*) as order_count,
        SUM(total_amount) as total_revenue
      FROM orders
      WHERE
        {% condition order_date %} DATE(created_at) {% endcondition %}
      GROUP BY 1, 2
    ;;

    # Incremental strategy
    datagroup_trigger: hourly_refresh
    increment_key: "order_date"
    increment_offset: 3

    # SQL for incremental loads
    sql_trigger_value: SELECT CURRENT_DATE ;;
    partition_keys: ["order_date"]
  }

  dimension_group: order {
    type: time
    timeframes: [date, week, month]
    sql: ${TABLE}.order_date ;;
  }

  filter: order_date {
    type: date
  }
}

ネイティブ派生テーブル（NDT）：

explore: orders {
  # Inline derived table
  join: order_summary {
    type: left_outer
    sql_on: ${orders.id} = ${order_summary.order_id} ;;
    relationship: one_to_one

    sql_table_name:
      (SELECT
        order_id,
        COUNT(*) as item_count,
        SUM(quantity) as total_quantity
      FROM order_items
      GROUP BY order_id)
    ;;
  }
}

Explore と結合

高度な結合パターン：

explore: orders {
  # Many-to-one join
  join: users {
    type: left_outer
    sql_on: ${orders.user_id} = ${users.id} ;;
    relationship: many_to_one
  }

  # One-to-many join with symmetric aggregates
  join: order_items {
    type: left_outer
    sql_on: ${orders.id} = ${order_items.order_id} ;;
    relationship: one_to_many
  }

  # Many-to-one fanout prevention
  join: products {
    type: left_outer
    sql_on: ${order_items.product_id} = ${products.id} ;;
    relationship: many_to_one
    required_joins: [order_items]
  }

  # Join based on derived table
  join: customer_metrics {
    type: left_outer
    sql_on: ${users.id} = ${customer_metrics.user_id} ;;
    relationship: one_to_one
  }

  # Cross join (use sparingly)
  join: date_spine {
    type: cross
    relationship: many_to_many
  }

  # Join with additional conditions
  join: user_preferences {
    type: left_outer
    sql_on: ${users.id} = ${user_preferences.user_id}
           AND ${user_preferences.is_active} = true ;;
    relationship: one_to_one
  }
}

リファインメント：

# Extend base explore
explore: +orders {
  label: "Orders Extended"

  # Add additional join
  join: promotions {
    type: left_outer
    sql_on: ${orders.promotion_id} = ${promotions.id} ;;
    relationship: many_to_one
  }

  # Override existing join
  join: users {
    fields: [users.id, users.name, users.email]  # Limit fields
  }
}

パラメーターとテンプレートフィルター

パラメーター：

view: orders {
  # Parameter for dynamic measures
  parameter: metric_selector {
    type: unquoted
    allowed_value: {
      label: "Revenue"
      value: "revenue"
    }
    allowed_value: {
      label: "Order Count"
      value: "count"
    }
    allowed_value: {
      label: "Average Order Value"
      value: "aov"
    }
  }

  measure: dynamic_metric {
    label_from_parameter: metric_selector
    type: number
    sql:
      {% if metric_selector._parameter_value == 'revenue' %}
        ${total_revenue}
      {% elsif metric_selector._parameter_value == 'count' %}
        ${count}
      {% elsif metric_selector._parameter_value == 'aov' %}
        ${average_order_value}
      {% else %}
        NULL
      {% endif %}
    ;;
  }

  # Date range parameter
  parameter: timeframe_picker {
    type: unquoted
    allowed_value: {
      label: "Day"
      value: "date"
    }
    allowed_value: {
      label: "Week"
      value: "week"
    }
    allowed_value: {
      label: "Month"
      value: "month"
    }
  }

  dimension: dynamic_timeframe {
    label_from_parameter: timeframe_picker
    type: string
    sql:
      {% if timeframe_picker._parameter_value == 'date' %}
        ${created_date}
      {% elsif timeframe_picker._parameter_value == 'week' %}
        ${created_week}
      {% elsif timeframe_picker._parameter_value == 'month' %}
        ${created_month}
      {% else %}
        ${created_date}
      {% endif %}
    ;;
  }
}

テンプレートフィルター：

view: orders {
  # Filter-only field
  filter: date_filter {
    type: date
    description: "Use this filter to define date range"
  }

  # Dimension using filter
  dimension: is_in_date_range {
    type: yesno
    sql: {% condition date_filter %} ${created_raw} {% endcondition %} ;;
  }

  # Measure using filter
  measure: orders_in_range {
    type: count
    filters: [is_in_date_range: "yes"]
  }

  # Multiple filter conditions
  filter: amount_range {
    type: number
  }

  measure: orders_in_amount_range {
    type: count
    sql: ${id} ;;
    filters: [
      status: "completed"
    ]
    sql: {% condition amount_range %} ${total_amount} {% endcondition %} ;;
  }
}

ダッシュボード

ダッシュボードの定義：

# dashboards/executive_overview.dashboard.lookml
- dashboard: executive_overview
  title: Executive Overview
  layout: newspaper
  preferred_viewer: dashboards-next

  filters:
  - name: date_range
    title: Date Range
    type: field_filter
    default_value: last 30 days
    allow_multiple_values: true
    required: false
    model: analytics
    explore: orders
    field: orders.created_date

  - name: region
    title: Region
    type: field_filter
    default_value: ""
    allow_multiple_values: true
    required: false
    model: analytics
    explore: orders
    field: users.region

  elements:
  # Revenue tile
  - name: total_revenue
    title: Total Revenue
    model: analytics
    explore: orders
    type: single_value
    fields: [orders.total_revenue]
    filters:
      orders.status: completed
    sorts: [orders.total_revenue desc]
    limit: 500
    listen:
      date_range: orders.created_date
      region: users.region
    row: 0
    col: 0
    width: 6
    height: 4

  # Order count tile
  - name: order_count
    title: Total Orders
    model: analytics
    explore: orders
    type: single_value
    fields: [orders.count]
    listen:
      date_range: orders.created_date
      region: users.region
    row: 0
    col: 6
    width: 6
    height: 4

  # Revenue trend chart
  - name: revenue_trend
    title: Revenue Trend
    model: analytics
    explore: orders
    type: looker_line
    fields: [orders.created_date, orders.total_revenue, orders.count]
    fill_fields: [orders.created_date]
    sorts: [orders.created_date desc]
    limit: 500
    x_axis_gridlines: false
    y_axis_gridlines: true
    show_view_names: false
    show_y_axis_labels: true
    show_y_axis_ticks: true
    y_axis_tick_density: default
    y_axis_tick_density_custom: 5
    show_x_axis_label: true
    show_x_axis_ticks: true
    y_axis_scale_mode: linear
    x_axis_reversed: false
    y_axis_reversed: false
    plot_size_by_field: false
    trellis: ''
    stacking: ''
    limit_displayed_rows: false
    legend_position: center
    point_style: none
    show_value_labels: false
    label_density: 25
    x_axis_scale: auto
    y_axis_combined: true
    show_null_points: true
    interpolation: linear
    y_axes: [{label: Revenue, orientation: left, series: [{axisId: orders.total_revenue,
            id: orders.total_revenue, name: Total Revenue}], showLabels: true, showValues: true,
        unpinAxis: false, tickDensity: default, tickDensityCustom: 5, type: linear}]
    series_colors:
      orders.total_revenue: "#1f77b4"
    listen:
      date_range: orders.created_date
      region: users.region
    row: 4
    col: 0
    width: 12
    height: 6

  # Top products table
  - name: top_products
    title: Top Products
    model: analytics
    explore: orders
    type: looker_grid
    fields: [products.name, order_items.total_quantity, order_items.total_revenue]
    sorts: [order_items.total_revenue desc]
    limit: 10
    show_view_names: false
    show_row_numbers: true
    transpose: false
    truncate_text: true
    hide_totals: false
    hide_row_totals: false
    size_to_fit: true
    table_theme: white
    limit_displayed_rows: false
    enable_conditional_formatting: true
    header_text_alignment: left
    header_font_size: '12'
    rows_font_size: '12'
    conditional_formatting: [{type: along a scale..., value: !!null '', background_color: !!null '',
        font_color: !!null '', color_application: {collection_id: default, palette_id: default-sequential-0},
        bold: false, italic: false, strikethrough: false, fields: [order_items.total_revenue]}]
    listen:
      date_range: orders.created_date
      region: users.region
    row: 10
    col: 0
    width: 12
    height: 6

アクセス制御とセキュリティ

ユーザー属性：

# Access filters based on user attributes
explore: orders {
  access_filter: {
    field: users.country
    user_attribute: country
  }

  access_filter: {
    field: users.region
    user_attribute: user_region
  }

  # SQL always where with user attribute
  sql_always_where:
    {% if _user_attributes['department'] == 'finance' %}
      ${orders.status} = 'completed'
    {% else %}
      1=1
    {% endif %}
  ;;
}

フィールドレベルのセキュリティ：

view: users {
  dimension: email {
    type: string
    sql: ${TABLE}.email ;;
    # Hide from non-admin users
    required_access_grants: [admin_only]
  }

  dimension: ssn {
    type: string
    sql: ${TABLE}.ssn ;;
    # Multiple required grants (AND logic)
    required_access_grants: [admin_only, pii_access]
  }
}

# Define access grants
access_grant: admin_only {
  user_attribute: role
  allowed_values: ["admin", "super_admin"]
}

access_grant: pii_access {
  user_attribute: can_see_pii
  allowed_values: ["yes"]
}

ベストプラクティス

1. ビューの設計

• すべてのビューにプライマリーキーを使用します
• 日付にはディメンショングループを作成します
• すべてのフィールドに説明を追加します
• 一貫した書式設定には value_format_name を使用します
• 技術的なフィールドをユーザーに表示しないようにします
• 探索パスには drill_fields を使用します

2. Explore の設計

• ディメンションテーブルとファクトテーブルを適切に結合します
• 正しいリレーションシップタイプを理解し、使用します
• 1 対多の結合には対称集計を使用します
• データのフィルタリングには sql_always_where を適用します
• 適切な always_filter のデフォルト値を設定します
• パフォーマンス向上には集計認識を使用します

3. パフォーマンス

• 複雑な計算には永続派生テーブルを使用します
• よく使われるクエリには集計テーブルを実装します
• キャッシュには適切な datagroup を設定します
• PDT の結合キーにインデックスを使用します
• Explore で公開するフィールドを制限します
• 遅いクエリを監視して最適化します

4. 保守性

• 一貫した命名規則を使用します
• ドメインごとにビューを整理します
• extends を使って再利用可能なディメンションを作成します
• 複雑なロジックを文書化します
• 重複を避けるためにリファインメントを使用します
• LookML を Git でバージョン管理します

5. ガバナンス

• ユーザー属性を使ってアクセス制御を実装します
• 機密データにはフィールドレベルのセキュリティを使用します
• 対象ユーザーごとに厳選した Explore を作成します
• データリネージを文書化します
• 命名規則を策定します

アンチパターン

1. 対称集計の問題

# Bad: Incorrect fanout handling
measure: total_items {
  type: sum
  sql: ${order_items.quantity} ;;  # Will double-count with 1-to-many join
}

# Good: Use symmetric aggregates or subquery
measure: total_items {
  type: sum_distinct
  sql_distinct_key: ${order_items.id} ;;
  sql: ${order_items.quantity} ;;
}

2. プライマリーキーを使用しない

# Bad: No primary key
view: users {
  dimension: id { type: number }
}

# Good: Define primary key
view: users {
  dimension: id {
    primary_key: yes
    type: number
  }
}

3. 値のハードコード

# Bad: Hardcoded logic
dimension: is_current_year {
  sql: YEAR(${created_date}) = 2024 ;;
}

# Good: Dynamic logic
dimension: is_current_year {
  sql: YEAR(${created_date}) = YEAR(CURRENT_DATE) ;;
}

4. 説明の不足

# Bad: No documentation
dimension: ltv { type: number sql: ${TABLE}.ltv ;; }

# Good: Clear documentation
dimension: ltv {
  type: number
  sql: ${TABLE}.ltv ;;
  label: "Lifetime Value"
  description: "Total revenue from customer over all time"
  value_format_name: usd
}

リソース

• LookML リファレンス
• Looker のベストプラクティス
• Looker コミュニティ
• Looker Discourse
• LookML バリデーター
• Looker API ドキュメント
• Looker GitHub