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title DynamoDB Data Connector
sidebar_label DynamoDB Data Connector
description DynamoDB Data Connector Documentation
tags
data-connectors
dynamodb
nosql
component-metrics

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. This connector enables using DynamoDB tables as data sources for federated SQL queries in Spice.

datasets:
  - from: dynamodb:users
    name: users
    params:
      dynamodb_aws_region: us-west-2
      dynamodb_aws_access_key_id: ${secrets:aws_access_key_id} # Optional
      dynamodb_aws_secret_access_key: ${secrets:aws_secret_access_key} # Optional
      dynamodb_aws_session_token: ${secrets:aws_session_token} # Optional

Configuration

from

The from field should specify the DynamoDB table name:

from Description
dynamodb:table Read data from a DynamoDB table named table

:::note If an expected table is not found, verify the dynamodb_aws_region parameter. DynamoDB tables are region-specific. :::

name

The dataset name. This will be used as the table name within Spice.

Example:

datasets:
  - from: dynamodb:users
    name: my_users
    params: ...
SELECT COUNT(*) FROM my_users;

The dataset name cannot be a [reserved keyword(../../reference/spicepod/keywords).

params

The DynamoDB data connector supports the following configuration parameters:

Parameter Name Description
dynamodb_aws_region Required. The AWS region containing the DynamoDB table
dynamodb_aws_access_key_id Optional. AWS access key ID for authentication. If not provided, credentials will be loaded from environment variables or IAM roles
dynamodb_aws_secret_access_key Optional. AWS secret access key for authentication. If not provided, credentials will be loaded from environment variables or IAM roles
dynamodb_aws_session_token Optional. AWS session token for authentication
unnest_depth Optional. Maximum nesting depth for unnesting embedded documents into a flattened structure. Higher values expand deeper nested fields.
schema_infer_max_records Optional. The number of documents to use to infer the schema. Defaults to 10
scan_segments Optional. Number of segments for Scan request. 'auto' by default, which will calculate number of segments based on number of the records in a table

Authentication

If AWS credentials are not explicitly provided in the configuration, the connector will automatically load credentials from the following sources in order.

  1. Environment Variables:

    • AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
    • AWS_SESSION_TOKEN (if using temporary credentials)

  2. Shared AWS Config/Credentials Files:

    • Config file: ~/.aws/config (Linux/Mac) or %UserProfile%\.aws\config (Windows)

    • Credentials file: ~/.aws/credentials (Linux/Mac) or %UserProfile%\.aws\credentials (Windows)

    • The AWS_PROFILE environment variable can be used to specify a named profile, otherwise the [default] profile is used.

    • Supports both static credentials and SSO sessions

    • Example credentials file:

      # Static credentials
[default]
aws_access_key_id = YOUR_ACCESS_KEY
aws_secret_access_key = YOUR_SECRET_KEY

# SSO profile
[profile sso-profile]
sso_start_url = https://my-sso-portal.awsapps.com/start
sso_region = us-west-2
sso_account_id = 123456789012
sso_role_name = MyRole
region = us-west-2

    :::tip To set up SSO authentication:

    1. Run aws configure sso to configure a new SSO profile
    2. Use the profile by setting AWS_PROFILE=sso-profile
    3. Run aws sso login --profile sso-profile to start a new SSO session :::

  3. AWS STS Web Identity Token Credentials:

    • Used primarily with OpenID Connect (OIDC) and OAuth
    • Common in Kubernetes environments using IAM roles for service accounts (IRSA)

  4. ECS Container Credentials:

    • Used when running in Amazon ECS containers
    • Automatically uses the task's IAM role
    • Retrieved from the ECS credential provider endpoint
    • Relies on the environment variable AWS_CONTAINER_CREDENTIALS_RELATIVE_URI or AWS_CONTAINER_CREDENTIALS_FULL_URI which are automatically injected by ECS.

  5. AWS EC2 Instance Metadata Service (IMDSv2):

    • Used when running on EC2 instances.
    • Automatically uses the instance's IAM role.
    • Retrieved securely using IMDSv2.

The connector will try each source in order until valid credentials are found. If no valid credentials are found, an authentication error will be returned.

:::note[IAM Permissions] Regardless of the credential source, the IAM role or user must have appropriate S3 permissions (e.g., s3:ListBucket, s3:GetObject) to access the files. If the Spicepod connects to multiple different AWS services, the permissions should cover all of them. :::

Required IAM Permissions

The IAM role or user needs the following permissions to access DynamoDB tables:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "dynamodb:Scan",
                "dynamodb:Query",
                "dynamodb:DescribeTable"
            ],
            "Resource": [
                "arn:aws:dynamodb:*:*:table/YOUR_TABLE_NAME"
            ]
        }
    ]
}

Permission Details

Permission Purpose
dynamodb:Scan Required. Allows reading all items from the table
dynamodb:Query Required. Allows reading items from the table using partition key
dynamodb:DescribeTable Required. Allows fetching table metadata and schema information

Example IAM Policies

Minimal Policy (Read-only access to specific table)

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "dynamodb:Scan",
                "dynamodb:Query",
                "dynamodb:DescribeTable"
            ],
            "Resource": "arn:aws:dynamodb:us-west-2:123456789012:table/users"
        }
    ]
}

Access to Multiple Tables

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "dynamodb:Scan",
                "dynamodb:Query",
                "dynamodb:DescribeTable"
            ],
            "Resource": [
                "arn:aws:dynamodb:us-west-2:123456789012:table/users",
                "arn:aws:dynamodb:us-west-2:123456789012:table/orders"
            ]
        }
    ]
}

Access to All Tables in a Region

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "dynamodb:Scan",
                "dynamodb:Query",
                "dynamodb:DescribeTable"
            ],
            "Resource": "arn:aws:dynamodb:us-west-2:123456789012:table/*"
        }
    ]
}

:::warning[Security Considerations]

:::

Data Types

The table below shows the DynamoDB data types supported, along with the type mapping to Apache Arrow types in Spice.

DynamoDB Type Description Arrow Type Notes
Bool Boolean Boolean
S String Utf8
S String Timestamp(Millisecond) Naive timestamp if it matches time_format without timezone
S String Timestamp(Millisecond, <timezone>) Timezone-aware timestamp if it matches time_format with timezone
Ss String Set List<Utf8>
N Number Int64 | Float64
Ns Number Set List<Int64|Float64>
B Binary Binary
Bs Binary Set List<Binary>
L List List<Utf8> DynamoDB arrays can be heterogeneous e.g. [1, "foo", true], Arrow arrays must be homogeneous - use strings to preserve all data
M Map Utf8 or Unflattened Depending on unnest_depth value

Time format

Since DynamoDB stores timestamps as strings, Spice supports parsing timestamps using a customizable format. By default, Spice will try to parse timestamps using ISO8601 format, but you can provide a custom format using the time_format parameter.

Once Spice is able to parse a timestamp, it will convert it to a Timestamp(Millisecond) Arrow type, and will use the same format to serialize it back to DynamoDB for filter pushdown.

This parameter uses Go-style time formatting, which uses a reference time of Mon Jan 2 15:04:05 MST 2006.

Format Pattern Example Value Description
2006-01-02T15:04:05.000Z07:00 2024-03-15T14:30:00.000Z ISO8601 / RFC3339 with milliseconds and timezone (default)
2006-01-02T15:04:05.999Z07:00 2024-03-15T14:30:00.123-07:00 ISO8601 with variable-length milliseconds and timezone
2006-01-02T15:04:05 2024-03-15T14:30:00 ISO8601 without timezone (naive timestamp)
2006-01-02 15:04:05 2024-03-15 14:30:00 Date and time with space separator
01/02/2006 15:04:05 03/15/2024 14:30:00 US-style date with time
02/01/2006 15:04:05 15/03/2024 14:30:00 European-style date with time
Jan 2, 2006 3:04:05 PM Mar 15, 2024 2:30:00 PM Human-readable with 12-hour clock
20060102150405 20240315143000 Compact format (no separators)

Go's format uses specific reference values that must appear exactly as shown:

Component Reference Value Alternatives
Year 2006 06 (2-digit)
Month 01 1, Jan, January
Day 02 2
Hour (24h) 15 —
Hour (12h) 03 3
Minute 04 4
Second 05 5
AM/PM PM pm
Timezone Z07:00 -0700, MST
Milliseconds .000 .999 (trailing zeros trimmed)
Microseconds .000000 .999999 (trailing zeros trimmed)
Nanoseconds .000000000 .999999999 (trailing zeros trimmed)

:::

Unnesting

Consider the following document:

{
  "a": 1,
  "b": {
    "x": 2,
    "y": {
      "z": 3
    }
  }
}

Using unnest_depth you can control the unnesting behavior. Here are the examples:

unnest_depth: 0

sql> select * from test_table;
+-----------+---------------------+
| a (Int32) | b (Utf8)            |
+-----------+---------------------+
| 1         | {"x":2,"y":{"z":3}} |
+---+-----------------------------+

unnest_depth: 1

sql> select * from test_table;
+-----------+-------------+------------+
| a (Int32) | b.x (Int32) | b.y (Utf8) |
+-----------+-------------+------------+
| 1         | 2           | {"z":3}    | 
+-----------+-------------+------------+

unnest_depth: 2

sql> select * from test_table;
+-----------+-------------+---------------+
| a (Int32) | b.x (Int32) | b.y.z (Int32) |
+-----------+-------------+---------------+
| 1         | 2           | 3             |
+-----------+-------------+---------------+

Examples

Basic Configuration with Environment Credentials

version: v1
kind: Spicepod
name: dynamodb

datasets:
  - from: dynamodb:users
    name: users
    params:
      dynamodb_aws_region: us-west-2
    acceleration:
      enabled: true

Configuration with Explicit Credentials

version: v1
kind: Spicepod
name: dynamodb

datasets:
  - from: dynamodb:users
    name: users
    params:
      dynamodb_aws_region: us-west-2
      dynamodb_aws_access_key_id: ${secrets:aws_access_key_id}
      dynamodb_aws_secret_access_key: ${secrets:aws_secret_access_key}
    acceleration:
      enabled: true

Configuration with time_format

version: v1
kind: Spicepod
name: dynamodb

datasets:
  - from: dynamodb:users
    name: users
    params:
      dynamodb_aws_region: us-west-2
      time_format: 2006-01-02 15:04:05
    acceleration:
      enabled: true

Querying Nested Structures

DynamoDB supports complex nested JSON structures. These fields can be queried using SQL:

-- Query nested structs
SELECT metadata.registration_ip, metadata.user_agent 
FROM users 
LIMIT 5;

-- Query nested structs in arrays
SELECT address.city
FROM (
    SELECT unnest(addresses) AS address 
    FROM users
)
WHERE address.city = 'San Francisco';

:::warning[Limitations]

  • The DynamoDB connector will scan the first 10 items to determine the schema of the table. This may miss columns that are not present in the first 10 items.
  • The DynamoDB connector does not support Decimal type.

:::

Example schema from a users table:

describe users;
+----------------+------------------+-------------+
| column_name    | data_type       | is_nullable |
+----------------+------------------+-------------+
| email          | Utf8            | YES         |
| id             | Int64           | YES         |
| metadata       | Struct          | YES         |
| addresses      | List(Struct)    | YES         |
| preferences    | Struct          | YES         |
| created_at     | Utf8            | YES         |
...
+----------------+------------------+-------------+

Streams

The DynamoDB Data Connector integrates with DynamoDB Streams to enable real-time streaming of table changes. This feature supports both initial table bootstrapping and continuous change data capture (CDC), allowing Spice to automatically detect and stream inserts, updates, and deletes from DynamoDB tables.

:::warning

Using DynamoDB Streams requires [acceleration(../data-accelerators/index) with refresh_mode: changes.

:::

Basic Configuration

To enable streaming from DynamoDB, enable acceleration and set the refresh_mode to changes in your dataset configuration.

You also need to configure the on_conflict parameter to specify how the connector should handle updates to existing records. The keys defined in on_conflict must match your DynamoDB table's partition key and range key (if your table has one)

datasets:
  - from: dynamodb:my_table
    name: orders_stream
    acceleration:
      enabled: true
      engine: duckdb
      mode: file
      refresh_mode: changes
      on_conflict:
        (id, version): upsert

Configuration Parameters

Dataset Parameters

  • ready_lag - Defines the maximum lag threshold before the dataset is reported as "Ready". Once the stream lag falls below this value, queries can be executed against the dataset. Default behavior reports ready immediately after bootstrap completes.

  • scan_interval - Controls the polling frequency for checking new records in the DynamoDB stream. Lower values provide more real-time updates but increase API calls. Higher values reduce API usage but may introduce additional latency.

Acceleration Parameters

  • on_conflict - Specifies the conflict resolution strategy when streaming changes that match existing records. The keys in the tuple should correspond to your DynamoDB table's partition key and range key (if applicable). The upsert action will insert new records or update existing ones based on these key columns.

    Examples:

    • Single partition key: id: upsert
    • Partition key + range key: (partition_key, sort_key): upsert

  • snapshots_trigger_threshold - Determines how frequently snapshots are created during streaming. A value of 5 means a snapshot is created every 5 batch updates. Snapshots enable faster recovery and better query performance but consume additional storage.

Metrics

The following Component Metrics are provided for monitoring streaming performance and health:

Metric Type Description
shards_active Gauge Current number of active shards in the stream
records_consumed_total Counter Total number of records consumed from the stream
lag_ms Gauge Current lag in milliseconds between stream watermark and the current time
errors_transient_total Counter Total number of transient errors encountered while polling from the stream

These metrics are not enabled by default, enable them by setting the metrics parameter:

datasets:
- from: kafka:user_events
  name: events
  metrics:
   - name: shards_active
   - name: lag_ms

You can find an example dashboard for DynamoDB Streams in monitoring/grafana-dashboard.json.

Advanced Configuration

For production workloads requiring fine-tuned control over streaming behavior and performance characteristics:

datasets:
   - from: dynamodb:my_table
     name: orders_stream
     params:
        ready_lag: 1s          # Dataset reports as Ready when lag is below 1 second
        scan_interval: 100ms   # Poll for new stream records every 100 milliseconds
     acceleration:
        enabled: true
        engine: duckdb
        mode: file
        refresh_mode: changes
        on_conflict:
           (id, version): upsert
        params:
           snapshots_trigger_threshold: 5  # Create snapshot every 5 batch updates
     metrics:
     - name: shards_active
       enabled: true
     - name: records_consumed_total
       enabled: true
     - name: lag_ms
       enabled: true
     - name: errors_transient_total
       enabled: true

Cookbooks