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Manage popularity

Popularity gives additional context to the usage of assets. From popularity, you can see various information such as:

  • users who have queried an asset, including:
    • those who have most recently queried the asset
    • those who have most frequently queried the asset
  • details about queries against the asset, including:
    • the most frequently run queries
    • the slowest-running queries
    • the most expensive queries

Popularity is only populated on certain assets by Atlan

Out-of-the-box, only specific crawlers currently populate popularity information on assets. However, the attributes involved are actually available behind-the-scenes on all assets.

Make details visible in the UI

Usage details not visible by default

By default, only the crawlers that support usage details out-of-the-box will show the usage details in the Atlan UI. To make usage details visible for other assets you must enable this at a connection level.

During connection creation

2.0.0 1.0.0

To create a popularity-enabled connection:

Create a popularity-enabled connection
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String adminRoleGuid = Atlan.getDefaultClient().getRoleCache().getIdForName("$admin"); // (1)
Connection connection = Connection.creator( // (2)
        "development", // (3)
        AtlanConnectorType.HIVE, // (4)
        List.of(adminRoleGuid), // (5)
        List.of("group2"), // (6)
        List.of("jsmith")) // (7)
    .hasPopularityInsights(true) // (8)
    .build();
AssetMutationResponse response = connection.save(); // (9)
  1. Retrieve the GUID for the admin role, to use later for defining the roles that can administer the connection.
  2. Build up the minimum request to create a connection.
  3. Provide a human-readable name for your connection.

  4. Set the type of connection.

    Determines the icon

    This determines the icon that Atlan will use for all the assets in the connection.

  5. List the workspace roles that should be able to administer the connection (or null if none). All users with that workspace role (current and future) will be administrators of the connection. Note that the values here need to be the GUID(s) of the workspace role(s). At least one of adminRoles, adminGroups, or adminUsers must be provided.

  6. List the group names that can administer this connection (or null if none). All users within that group (current and future) will be administrators of the connection. Note that the values here are the name(s) of the group(s). At least one of adminRoles, adminGroups, or adminUsers must be provided.
  7. List the user names that can administer this connection (or null if none). Note that the values here are the username(s) of the user(s). At least one of adminRoles, adminGroups, or adminUsers must be provided.
  8. Set the hasPopularityInsights property to true — this is the key piece to ensuring usage details will be visible in the UI for assets in this connection.
  9. Actually call Atlan to create the connection.
Create a relational connection
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from pyatlan.cache.role_cache import RoleCache
from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import Connection
from pyatlan.model.enums import AtlanConnectorType

admin_role_guid = RoleCache.get_id_for_name("$admin") # (1)
connection = Connection.creator( # (2)
    name="development", # (3)
    connector_type=AtlanConnectorType.HIVE, # (4)
    admin_roles=[admin_role_guid], # (5)
    admin_groups=["group2"], # (6)
    admin_users=["jsmith"], # (7)
)
connection.has_popularity_insights = True  # (8)
response = client.asset.save(connection) # (9)
  1. Retrieve the GUID for the admin role, to use later for defining the roles that can administer the connection.
  2. Build up the minimum request to create a connection.
  3. Provide a human-readable name for your connection.

  4. Set the type of connection.

    Determines the icon

    This determines the icon that Atlan will use for all the assets in the connection.

  5. List the workspace roles that should be able to administer the connection (if any, defaults to None). All users with that workspace role (current and future) will be administrators of the connection. Note that the values here need to be the GUID(s) of the workspace role(s). At least one of admin_roles, admin_groups, or admin_users must be provided.

  6. List the group names that can administer this connection (if any, defaults to None). All users within that group (current and future) will be administrators of the connection. Note that the values here are the name(s) of the group(s). At least one of admin_roles, admin_groups, or admin_users must be provided.
  7. List the user names that can administer this connection (if any, defaults to None). Note that the values here are the username(s) of the user(s). At least one of admin_roles, admin_groups, or admin_users must be provided.
  8. Set the has_popularity_insights property to True — this is the key piece to ensuring usage details will be visible in the UI for assets in this connection.
  9. Actually call Atlan to create the connection.
Create a popularity-enabled connection
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val adminRoleGuid = Atlan.getDefaultClient().roleCache.getIdForName("\$admin") // (1)
val connection = Connection.creator( // (2)
        "development",  // (3)
        AtlanConnectorType.HIVE,  // (4)
        java.util.List.of(adminRoleGuid),  // (5)
        listOf("group2"),  // (6)
        listOf("jsmith")) // (7)
    .hasPopularityInsights(true) // (8)
    .build()
val response: AssetMutationResponse = connection.save() // (9)
  1. Retrieve the GUID for the admin role, to use later for defining the roles that can administer the connection.
  2. Build up the minimum request to create a connection.
  3. Provide a human-readable name for your connection.

  4. Set the type of connection.

    Determines the icon

    This determines the icon that Atlan will use for all the assets in the connection.

  5. List the workspace roles that should be able to administer the connection (or null if none). All users with that workspace role (current and future) will be administrators of the connection. Note that the values here need to be the GUID(s) of the workspace role(s). At least one of adminRoles, adminGroups, or adminUsers must be provided.

  6. List the group names that can administer this connection (or null if none). All users within that group (current and future) will be administrators of the connection. Note that the values here are the name(s) of the group(s). At least one of adminRoles, adminGroups, or adminUsers must be provided.
  7. List the user names that can administer this connection (or null if none). Note that the values here are the username(s) of the user(s). At least one of adminRoles, adminGroups, or adminUsers must be provided.
  8. Set the hasPopularityInsights property to true — this is the key piece to ensuring usage details will be visible in the UI for assets in this connection.
  9. Actually call Atlan to create the connection.
POST /api/meta/entity/bulk
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{
  "entities": [
    {
      "typeName": "Connection", // (1)
      "attributes": {
        "name": "development", // (2)
        "connectorName": "hive", // (3)
        "qualifiedName": "default/hive/123456789", // (4)
        "category": "warehouse", // (5)
        "adminRoles": [ // (6)
          "e7ae0295-c60a-469a-bd2c-fb903943aa02"
        ],
        "adminGroups": [ // (7)
          "group2"
        ],
        "adminUsers": [ // (8)
          "jsmith"
        ],
        "hasPopularityInsights": true // (9)
      }
    }
  ]
}
  1. The typeName must be exactly Connection.
  2. Human-readable name for your connection.

  3. The connectorName should be a known value, such as hive.

    Determines the icon

    This determines the icon that Atlan will use for all the assets in the connection. If you use a value that is not a known value, you will have a default gear icon instead.

  4. The qualifiedName should follow the pattern: default/<connectorName>/<epoch>, where <epoch> is the time in milliseconds at which the connection is being created, and <connectorName> exactly matches the value used for connectorName (above).

  5. The category should also be a known value, that defines the kind of relational store. This could for example be warehouse or rdbms.
  6. List any workspace roles that can administer this connection. All users with that workspace role (current and future) will be administrators of the connection. Note that the values here need to be the GUID(s) of the workspace role(s). At least one of adminRoles, adminGroups, or adminUsers must be provided.
  7. List any groups that can administer this connection. All users within that group (current and future) will be administrators of the connection. Note that the values here are the name(s) of the group(s). At least one of adminRoles, adminGroups, or adminUsers must be provided.
  8. List any users that can administer this connection. Note that the values here are the username(s) of the user(s). At least one of adminRoles, adminGroups, or adminUsers must be provided.
  9. Set the hasPopularityInsights property to true — this is the key piece to ensuring usage details will be visible in the UI for assets in this connection.

Update an existing connection

2.0.0 1.0.0

To update an existing connection:

Update connection to make popularity visible
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Connection connection = Connection.get("default/hive/1657025257"); // (1)
AssetMutationResponse updated = connection.trimToRequired() // (2)
    .hasPopularityInsights(true) // (3)
    .build() // (4)
    .save(); // (5)
  1. Build an object referencing the existing connection. In this example we will retrieve it first, but you could also search for it or use the updater() method to construct an update.
  2. If starting from an existing object, remember to trimToRequired() to get a builder with the minimal required information for an update.
  3. Set the hasPopularityInsights property to true.
  4. Build the object, so it is ready to be persisted.
  5. Persist the object by saving it.
Update connection to make popularity visible
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from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import Connection

client = AtlanClient()

connection = client.asset.get_by_qualified_name(  # (1)
    qualified_name="default/hive/1657025257",
    asset_type=Connection
).trim_to_required()  # (2)
connection.has_popularity_insights = True  # (3)
updated = client.asset.save(connection)  # (4)
  1. Build an object referencing the existing connection. In this example we will retrieve it first, but you could also search for it or use the updater() method to construct an update.
  2. If starting from an existing object, remember to trim_to_required() to get an asset with the minimal required information for an update.
  3. Set the has_popularity_insights property to True.
  4. Persist the object by saving it.
Update connection to make popularity visible
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val connection = Connection.get("default/hive/1657025257") // (1)
val updated = connection.trimToRequired() // (2)
    .hasPopularityInsights(true) // (3)
    .build() // (4)
    .save() // (5)
  1. Build an object referencing the existing connection. In this example we will retrieve it first, but you could also search for it or use the updater() method to construct an update.
  2. If starting from an existing object, remember to trimToRequired() to get a builder with the minimal required information for an update.
  3. Set the hasPopularityInsights property to true.
  4. Build the object, so it is ready to be persisted.
  5. Persist the object by saving it.
POST /api/meta/entity/bulk
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{
  "entities": [ // (1)
    {
      "typeName": "Connection", // (2)
      "attributes": { // (3)
        "qualifiedName": "default/hive/1657025257", // (4)
        "name": "development", // (5)
        "hasPopularityInsights": true // (6)
      }
    }
  ]
}
  1. The connection must be wrapped in an entities array.
  2. You must use a typeName of Connection to update the connection.
  3. You must then wrap additional details in an attributes object.
  4. You must provide the exact qualifiedName of the connection (case-sensitive).
  5. You must provide the exact name of the connection (case-sensitive).
  6. Set the hasPopularityInsights property to true.

Retrieve usage details

1.4.0 1.0.0

Since popularity details are only available on certain assets, you will need to retrieve one of those assets to see the usage details:

Retrieve popularity
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Table table = Table.get( // (1)
        "default/hive/1657025257/OPS/DEFAULT/RUN_STATS"); // (2)
table.getPopularityScore(); // (3)
table.getSourceReadCount();
table.getSourceReadQueryCost();
table.getSourceReadUserCount();
table.getSourceTotalCost();
table.getSourceQueryComputeCosts(); // (4)
table.getSourceReadRecentUsers();
table.getSourceReadTopUsers();
table.getSourceQueryComputeCostRecords(); // (5)
table.getSourceReadRecentUserRecords();
table.getSourceReadTopUserRecords();
table.getSourceReadExpensiveQueryRecords(); // (6)
table.getSourceReadPopularQueryRecords();
table.getSourceReadSlowQueryRecords();
  1. Use the get() method to retrieve all details about a specific asset.
  2. Provide the full, case-sensitive qualifiedName of the asset.

  3. You can retrieve single quantified metrics for various aspects, such as:

    • An overall popularity score for the asset (higher is better), using getPopularityScore().
    • The total number of all read operations on the asset, using getSourceReadCount().
    • The total cost of all read operations on the asset, using getSourceReadQueryCost().
    • The total number of unique users that read data from the asset, using getSourceReadUserCount().
    • The total cost of all operations on the asset, using getSourceTotalCost().

  4. You can retrieve lists of various aspects, such as:

    • The top query running engines (warehouses), using getSourceQueryComputeCosts().
    • The users who most recently queried the asset, using getSourceReadRecentUsers().
    • The users who most frequently query the asset, using getSourceReadTopUsers().

  5. You can also list more detailed information about individual metrics, such as:

    • The top query running engines (warehouses) and their related cost, using getSourceQueryComputeCostRecords().
    • The users who most recently queried the asset, the number of times they've queried it and when they last queried it, using getSourceReadRecentUserRecords().
    • The users who most frequently query the asset, the number of times they've queried it and when they last queried it, using getSourceReadTopUserRecords().

  6. You can also list details about specific queries, such as:

    • The list of most expensive queries against this asset, using getSourceReadExpensiveQueryRecords().
    • The list of the most frequently run queries against this asset, using getSourceReadPopularQueryRecords().
    • The list of the slowest-running queries against this asset, using getSourceReadSlowQueryRecords().
Retrieve popularity
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from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import Table

client = AtlanClient()

table = client.asset.get_by_qualified_name( # (1)
    qualified_name="default/hive/1657025257/OPS/DEFAULT/RUN_STATS", # (2)
    asset_type=Table
)
table.popularity_score # (3)
table.source_read_count
table.source_read_query_cost
table.source_read_user_count
table.source_total_cost
table.source_query_compute_cost_list # (4)
table.source_read_recent_user_list
table.source_read_top_user_list
table.source_query_compute_cost_record_list # (5)
table.source_read_recent_user_record_list
table.source_read_top_user_record_list
table.source_read_expensive_query_record_list # (6)
table.source_read_popular_query_record_list
table.source_read_slow_query_record_list
  1. Use the asset.get_by_qualified_name() method to retrieve all details about a specific asset.
  2. Provide the full, case-sensitive qualified_name of the asset, and the type of the asset.

  3. You can retrieve single quantified metrics for various aspects, such as:

    • An overall popularity score for the asset (higher is better), using popularity_score.
    • The total number of all read operations on the asset, using source_read_count.
    • The total cost of all read operations on the asset, using source_read_query_cost.
    • The total number of unique users that read data from the asset, using source_read_user_count.
    • The total cost of all operations on the asset, using source_total_cost.

  4. You can retrieve lists of various aspects, such as:

    • The top query running engines (warehouses), using source_query_compute_cost_list.
    • The users who most recently queried the asset, using source_read_recent_user_list.
    • The users who most frequently query the asset, using source_read_top_user_list.

  5. You can also list more detailed information about individual metrics, such as:

    • The top query running engines (warehouses) and their related cost, using source_query_compute_cost_record_list.
    • The users who most recently queried the asset, the number of times they've queried it and when they last queried it, using source_read_recent_user_record_list.
    • The users who most frequently query the asset, the number of times they've queried it and when they last queried it, using source_read_top_user_record_list.

  6. You can also list details about specific queries, such as:

    • The list of most expensive queries against this asset, using source_read_expensive_query_record_list.
    • The list of the most frequently run queries against this asset, using source_read_popular_query_record_list.
    • The list of the slowest-running queries against this asset, using source_read_slow_query_record_list.
Retrieve popularity
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val table = Table.get( // (1)
    "default/hive/1657025257/OPS/DEFAULT/RUN_STATS") // (2)
table.popularityScore // (3)
table.sourceReadCount
table.sourceReadQueryCost
table.sourceReadUserCount
table.sourceTotalCost
table.sourceQueryComputeCosts // (4)
table.sourceReadRecentUsers
table.sourceReadTopUsers
table.sourceQueryComputeCostRecords // (5)
table.sourceReadRecentUserRecords
table.sourceReadTopUserRecords
table.sourceReadExpensiveQueryRecords // (6)
table.sourceReadPopularQueryRecords
table.sourceReadSlowQueryRecords
  1. Use the get() method to retrieve all details about a specific asset.
  2. Provide the full, case-sensitive qualifiedName of the asset.

  3. You can retrieve single quantified metrics for various aspects, such as:

    • An overall popularity score for the asset (higher is better), using .popularityScore.
    • The total number of all read operations on the asset, using .sourceReadCount.
    • The total cost of all read operations on the asset, using .sourceReadQueryCost.
    • The total number of unique users that read data from the asset, using .sourceReadUserCount.
    • The total cost of all operations on the asset, using .sourceTotalCost.

  4. You can retrieve lists of various aspects, such as:

    • The top query running engines (warehouses), using .sourceQueryComputeCosts.
    • The users who most recently queried the asset, using .sourceReadRecentUsers.
    • The users who most frequently query the asset, using .sourceReadTopUsers.

  5. You can also list more detailed information about individual metrics, such as:

    • The top query running engines (warehouses) and their related cost, using .sourceQueryComputeCostRecords.
    • The users who most recently queried the asset, the number of times they've queried it and when they last queried it, using .sourceReadRecentUserRecords.
    • The users who most frequently query the asset, the number of times they've queried it and when they last queried it, using .sourceReadTopUserRecords.

  6. You can also list details about specific queries, such as:

    • The list of most expensive queries against this asset, using .sourceReadExpensiveQueryRecords.
    • The list of the most frequently run queries against this asset, using .sourceReadPopularQueryRecords.
    • The list of the slowest-running queries against this asset, using .sourceReadSlowQueryRecords.
GET /api/meta/entity/uniqueAttribute/type/Table?attr:qualifiedName=default%2Fhive%2F1657025257%2FOPS%2FDEFAULT%2FRUN_STATS
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{
  "entity": { // (1)
    "typeName": "Table", // (2)
    "attributes": { // (3)
      "name": "RUN_STATS",
      "qualifiedName": "default/hive/1657025257/OPS/DEFAULT/RUN_STATS",
      "popularityScore": 4.8121843, // (4)
      "sourceReadCount": 122,
      "sourceReadQueryCost": 0.001278,
      "sourceReadUserCount": 1,
      "sourceTotalCost": 0.001278,
      "sourceLastReadAt": 1689508975245,
      "sourceCostUnit": "Credits",
      "sourceQueryComputeCostList": [
        "TRANSFORMING"
      ],
      "sourceReadTopUserList": [
        "ATLANADMIN"
      ],
      "sourceQueryComputeCostRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordTotalUserCount": 0,
            "recordUser": null,
            "recordQueryCount": 0,
            "recordWarehouse": "TRANSFORMING",
            "recordComputeCost": 0.001278,
            "recordLastTimestamp": 0,
            "recordQuery": null,
            "recordQueryDuration": 0,
            "recordMaxComputeCost": 0,
            "recordComputeCostUnit": "Credits"
          }
        }
      ],
      "sourceReadRecentUserRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordTotalUserCount": 0,
            "recordUser": "ATLANADMIN",
            "recordQueryCount": 122,
            "recordWarehouse": null,
            "recordComputeCost": 0,
            "recordLastTimestamp": 1689508975245,
            "recordQuery": null,
            "recordQueryDuration": 0,
            "recordMaxComputeCost": 0,
            "recordComputeCostUnit": null
          }
        }
      ],
      "sourceReadTopUserRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordTotalUserCount": 0,
            "recordUser": "ATLANADMIN",
            "recordQueryCount": 122,
            "recordWarehouse": null,
            "recordComputeCost": 0,
            "recordLastTimestamp": 1689508975245,
            "recordQuery": null,
            "recordQueryDuration": 0,
            "recordMaxComputeCost": 0,
            "recordComputeCostUnit": null
          }
        }
      ],
      "sourceReadExpensiveQueryRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordTotalUserCount": 1,
            "recordUser": null,
            "recordQueryCount": 36,
            "recordWarehouse": null,
            "recordComputeCost": 0.000407,
            "recordLastTimestamp": 0,
            "recordQuery": "SELECT * from RUN_STATS",
            "recordQueryDuration": 0,
            "recordMaxComputeCost": 4.8e-05,
            "recordComputeCostUnit": "Credits"
          }
        }
      ],
      "sourceReadPopularQueryRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordTotalUserCount": 1,
            "recordUser": null,
            "recordQueryCount": 36,
            "recordWarehouse": null,
            "recordComputeCost": 0.000309,
            "recordLastTimestamp": 0,
            "recordQuery": "SELECT * from RUN_STATS",
            "recordQueryDuration": 0,
            "recordMaxComputeCost": 0,
            "recordComputeCostUnit": "Credits"
          }
        }
      ],
      "sourceReadSlowQueryRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordTotalUserCount": 0,
            "recordUser": "ATLANADMIN",
            "recordQueryCount": 0,
            "recordWarehouse": null,
            "recordComputeCost": 0,
            "recordLastTimestamp": 1688584846605,
            "recordQuery": "SELECT * from RUN_STATS",
            "recordQueryDuration": 336,
            "recordMaxComputeCost": 0,
            "recordComputeCostUnit": null
          }
        }
      ]
    }
  }
}
  1. All asset details will come back wrapped in a top-level entity object in the payload.
  2. The typeName will indicate what kind of asset is returned.
  3. The detailed usage information will be embedded in the attributes object within the outer entity object.
  4. With the exception of the popularityScore, all other usage details have names that all start with source...

Add your own usage details

2.0.0 1.0.0

In cases where Atlan does not popularity details from the source, you may want to add your own. You can do this by either adding the usage details when creating the asset (programmatically) or by updating the attributes on an existing asset:

Add or update usage details
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Table table = Table.updater( // (1)
        "default/hive/1657025257/OPS/DEFAULT/RUN_STATS",
        "RUN_STATS")
    .popularityScore(1.2345) // (2)
    .sourceReadCount(123L)
    .sourceReadQueryCost(5.4321)
    .sourceReadUserCount(5L)
    .sourceTotalCost(5.4321)
    .sourceQueryComputeCosts(List.of("A", "B")) // (3)
    .sourceReadRecentUsers(List.of("c", "d", "e"))
    .sourceReadRecentUser("f")
    .sourceReadRecentUser("g")
    .sourceReadTopUsers(List.of("g", "f", "e"))
    .sourceReadTopUser("d")
    .sourceReadTopUser("c")
    .sourceQueryComputeCostRecord( // (4)
        PopularityInsights.builder()
            .recordComputeCost(4.4321)
            .recordComputeCostUnit(SourceCostUnitType.CREDITS)
            .recordWarehouse("A")
            .build())
    .sourceQueryComputeCostRecord(
        PopularityInsights.builder()
            .recordComputeCost(1.0)
            .recordComputeCostUnit(SourceCostUnitType.CREDITS)
            .recordWarehouse("B")
            .build())
    .sourceReadRecentUserRecord(
        PopularityInsights.builder()
            .recordUser("c")
            .recordLastTimestamp(1234567899000L)
            .build())
    .sourceReadRecentUserRecord(
        PopularityInsights.builder()
            .recordUser("d")
            .recordLastTimestamp(1234567898000L)
            .build())
    .sourceReadRecentUserRecord(
        PopularityInsights.builder()
            .recordUser("e")
            .recordLastTimestamp(1234567897000L)
            .recordQueryCount(3L)
            .build())
    .sourceReadTopUserRecord(
        PopularityInsights.builder()
            .recordUser("g")
            .recordQueryCount(100L)
            .recordLastTimestamp(1234567895000L)
            .build())
    .sourceReadTopUserRecord(
        PopularityInsights.builder()
            .recordUser("f")
            .recordQueryCount(20L)
            .recordLastTimestamp(1234567896000L)
            .build())
    .sourceReadTopUserRecord(
        PopularityInsights.builder()
            .recordUser("e")
            .recordQueryCount(3L)
            .recordLastTimestamp(1234567897000L)
            .build())
    .sourceReadExpensiveQueryRecord(
        PopularityInsights.builder()
            .recordComputeCost(5.4321)
            .recordMaxComputeCost(5.4321)
            .recordComputeCostUnit(SourceCostUnitType.CREDITS)
            .recordQuery("SELECT * from RUN_STATS")
            .recordQueryCount(123L)
            .recordTotalUserCount(5L)
            .build())
    .sourceReadPopularQueryRecord(
        PopularityInsights.builder()
            .recordComputeCost(5.4321)
            .recordComputeCostUnit(SourceCostUnitType.CREDITS)
            .recordQueryCount(123L)
            .recordQuery("SELECT * from RUN_STATS")
            .recordTotalUserCount(5L)
            .build())
    .sourceReadSlowQueryRecord(
        PopularityInsights.builder()
            .recordUser("g")
            .recordLastTimestamp(124567895000L)
            .recordQueryDuration(321L)
            .recordQuery("SELECT * from RUN_STATS")
            .build())
    .build();
AssetMutationResponse response = table.save(); // (5)
  1. Use the updater() method to update an existing asset, providing the required details for that particular asset type (for more details, see Updating an asset).
  2. For single quantified metrics, you can directly set the metric.
  3. For lists, you can directly set the lists or individually add elements (or a combination of the two).
  4. For the more detailed records, you need to build the PopularityInsights object with its embedded details. As with the lists you can associate these detailed records with the asset many-at-a-time or one-by-one.
  5. Finally, you must save the object you've built up to persist this information in Atlan.
Add or update usage details
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from pyatlan.client.atlan import AtlanClient
from pyatlan.model.assets import Table
from pyatlan.model.enums import SourceCostUnitType
from pyatlan.model.structs import PopularityInsights

client = AtlanClient()

table = Table.updater(  # (1)
    qualified_name="default/hive/1657025257/OPS/DEFAULT/RUN_STATS",
    name="RUN_STATS"
)
table.popularity_score = 1.2345  # (2)
table.source_read_count = 123
table.source_read_query_cost = 5.4321
table.source_read_user_count = 5
table.source_total_cost = 5.4321
table.source_query_compute_cost_list = ["A", "B"]  # (3)
table.source_read_recent_user_list = ["c", "d", "e", "f", "g"]
table.source_read_top_user_list = ["g", "f", "e", "d", "c"]
table.source_query_compute_cost_record_list = [  # (4)
    PopularityInsights(
        record_compute_cost=4.4321,
        record_compute_cost_unit=SourceCostUnitType.CREDITS,
        record_warehouse="A"
    ),
    PopularityInsights(
        record_compute_cost=1.0,
        record_compute_cost_unit=SourceCostUnitType.CREDITS,
        record_warehouse="B"
    )
]
table.source_read_recent_user_record_list = [
    PopularityInsights(
        record_user="c",
        record_last_timestamp=1234567899000
    ),
    PopularityInsights(
        record_user="d",
        record_last_timestamp=1234567898000
    ),
    PopularityInsights(
        record_user="e",
        record_last_timestamp=1234567897000,
        record_query_count=3
    )
]
table.source_read_top_user_record_list = [
    PopularityInsights(
        record_user="g",
        record_query_count=100,
        record_last_timestamp=1234567895000
    ),
    PopularityInsights(
        record_user="f",
        record_query_count=20,
        record_last_timestamp=1234567896000
    ),
    PopularityInsights(
        record_user="e",
        record_query_count=3,
        record_last_timestamp=1234567897000
    )
]
table.source_read_expensive_query_record_list = [
    PopularityInsights(
        record_compute_cost=5.4321,
        record_max_compute_cost=5.4321,
        record_compute_cost_unit=SourceCostUnitType.CREDITS,
        record_query="SELECT * from RUN_STATS",
        record_query_count=123,
        record_total_user_count=5
    )
]
table.source_read_popular_query_record_list = [
    PopularityInsights(
        record_compute_cost=5.4321,
        record_compute_cost_unit=SourceCostUnitType.CREDITS,
        record_query_count=123,
        record_query="SELECT * from RUN_STATS",
        record_total_user_count=5
    )
]
table.source_read_slow_query_record_list = [
    PopularityInsights(
        record_user="g",
        record_last_timestamp=1234567895000,
        record_query_duration=321,
        record_query="SELECT * from RUN_STATS"
    )
]
response = client.asset.save(table)  # (5)
  1. Use the updater() method to update an existing asset, providing the required details for that particular asset type (for more details, see Updating an asset).
  2. For single quantified metrics, you can directly set the metric.
  3. For lists, you can directly set the lists.
  4. For the more detailed records, you need to build each PopularityInsights object with its embedded details.
  5. Finally, you must save the object you've built up to persist this information in Atlan.
Add or update usage details
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val table = Table.updater( // (1)
        "default/hive/1657025257/OPS/DEFAULT/RUN_STATS",
        "RUN_STATS")
    .popularityScore(1.2345) // (2)
    .sourceReadCount(123L)
    .sourceReadQueryCost(5.4321)
    .sourceReadUserCount(5L)
    .sourceTotalCost(5.4321)
    .sourceQueryComputeCosts(List.of("A", "B")) // (3)
    .sourceReadRecentUsers(List.of("c", "d", "e"))
    .sourceReadRecentUser("f")
    .sourceReadRecentUser("g")
    .sourceReadTopUsers(List.of("g", "f", "e"))
    .sourceReadTopUser("d")
    .sourceReadTopUser("c")
    .sourceQueryComputeCostRecord( // (4)
        PopularityInsights.builder()
            .recordComputeCost(4.4321)
            .recordComputeCostUnit(SourceCostUnitType.CREDITS)
            .recordWarehouse("A")
            .build())
    .sourceQueryComputeCostRecord(
        PopularityInsights.builder()
            .recordComputeCost(1.0)
            .recordComputeCostUnit(SourceCostUnitType.CREDITS)
            .recordWarehouse("B")
            .build())
    .sourceReadRecentUserRecord(
        PopularityInsights.builder()
            .recordUser("c")
            .recordLastTimestamp(1234567899000L)
            .build())
    .sourceReadRecentUserRecord(
        PopularityInsights.builder()
            .recordUser("d")
            .recordLastTimestamp(1234567898000L)
            .build())
    .sourceReadRecentUserRecord(
        PopularityInsights.builder()
            .recordUser("e")
            .recordLastTimestamp(1234567897000L)
            .recordQueryCount(3L)
            .build())
    .sourceReadTopUserRecord(
        PopularityInsights.builder()
            .recordUser("g")
            .recordQueryCount(100L)
            .recordLastTimestamp(1234567895000L)
            .build())
    .sourceReadTopUserRecord(
        PopularityInsights.builder()
            .recordUser("f")
            .recordQueryCount(20L)
            .recordLastTimestamp(1234567896000L)
            .build())
    .sourceReadTopUserRecord(
        PopularityInsights.builder()
            .recordUser("e")
            .recordQueryCount(3L)
            .recordLastTimestamp(1234567897000L)
            .build())
    .sourceReadExpensiveQueryRecord(
        PopularityInsights.builder()
            .recordComputeCost(5.4321)
            .recordMaxComputeCost(5.4321)
            .recordComputeCostUnit(SourceCostUnitType.CREDITS)
            .recordQuery("SELECT * from RUN_STATS")
            .recordQueryCount(123L)
            .recordTotalUserCount(5L)
            .build())
    .sourceReadPopularQueryRecord(
        PopularityInsights.builder()
            .recordComputeCost(5.4321)
            .recordComputeCostUnit(SourceCostUnitType.CREDITS)
            .recordQueryCount(123L)
            .recordQuery("SELECT * from RUN_STATS")
            .recordTotalUserCount(5L)
            .build())
    .sourceReadSlowQueryRecord(
        PopularityInsights.builder()
            .recordUser("g")
            .recordLastTimestamp(124567895000L)
            .recordQueryDuration(321L)
            .recordQuery("SELECT * from RUN_STATS")
            .build())
    .build()
val response = table.save() // (5)
  1. Use the updater() method to update an existing asset, providing the required details for that particular asset type (for more details, see Updating an asset).
  2. For single quantified metrics, you can directly set the metric.
  3. For lists, you can directly set the lists or individually add elements (or a combination of the two).
  4. For the more detailed records, you need to build the PopularityInsights object with its embedded details. As with the lists you can associate these detailed records with the asset many-at-a-time or one-by-one.
  5. Finally, you must save the object you've built up to persist this information in Atlan.
POST /api/meta/entity/bulk
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{
  "entities": [{ // (1)
    "typeName": "Table", // (2)
    "attributes": {
      "name": "RUN_STATS", // (3)
      "qualifiedName": "default/hive/1657025257/OPS/DEFAULT/RUN_STATS", // (4)
      "popularityScore": 1.2345, // (5)
      "sourceReadCount": 123,
      "sourceReadQueryCost": 5.4321,
      "sourceReadUserCount": 5,
      "sourceTotalCost": 5.4321,
      "sourceLastReadAt": 1689508975245,
      "sourceCostUnit": "Credits",
      "sourceQueryComputeCostList": [ // (6)
        "A", "B"
      ],
      "sourceReadTopUserList": [
        "c", "d", "e", "f", "g"
      ],
      "sourceReadRecentUserList": [
        "g", "f", "e", "d", "c"
      ],
      "sourceQueryComputeCostRecordList": [ // (7)
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordWarehouse": "A",
            "recordComputeCost": 4.4321,
            "recordComputeCostUnit": "Credits"
          }
        },
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordWarehouse": "B",
            "recordComputeCost": 1.0,
            "recordComputeCostUnit": "Credits"
          }
        }
      ],
      "sourceReadRecentUserRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordUser": "c",
            "recordLastTimestamp": 1234567899000
          }
        },
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordUser": "d",
            "recordLastTimestamp": 1234567898000
          }
        },
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordUser": "e",
            "recordLastTimestamp": 1234567897000,
            "recordQueryCount": 3
          }
        }
      ],
      "sourceReadTopUserRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordUser": "g",
            "recordQueryCount": 100,
            "recordLastTimestamp": 1234567895000
          }
        },
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordUser": "f",
            "recordQueryCount": 20,
            "recordLastTimestamp": 1234567896000
          }
        },
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordUser": "e",
            "recordQueryCount": 3,
            "recordLastTimestamp": 1234567897000
          }
        }
      ],
      "sourceReadExpensiveQueryRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordTotalUserCount": 5,
            "recordQueryCount": 123,
            "recordComputeCost": 5.4321,
            "recordQuery": "SELECT * from RUN_STATS",
            "recordMaxComputeCost": 5.4321,
            "recordComputeCostUnit": "Credits"
          }
        }
      ],
      "sourceReadPopularQueryRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordTotalUserCount": 5,
            "recordQueryCount": 123,
            "recordComputeCost": 5.4321,
            "recordQuery": "SELECT * from RUN_STATS",
            "recordComputeCostUnit": "Credits"
          }
        }
      ],
      "sourceReadSlowQueryRecordList": [
        {
          "typeName": "PopularityInsights",
          "attributes": {
            "recordUser": "g",
            "recordLastTimestamp": 124567895000,
            "recordQuery": "SELECT * from RUN_STATS",
            "recordQueryDuration": 321
          }
        }
      ]
    }
  }]
}
  1. All assets must be wrapped in an entities array.
  2. The typeName must match the appropriate type for the asset being updated.
  3. You must provide the exact name of the asset (case-sensitive).
  4. You must provide the exact qualifiedName of the asset (case-sensitive).
  5. For single quantified metrics, you can directly set the metric.
  6. For lists, you can directly set the lists.
  7. For the more detailed records, you need to build each PopularityInsights object with its embedded details.