A Zero Administration AWS Lambda Based Amazon Redshift Database Loader

With this AWS Lambda function, it's never been easier to get file data into Amazon Redshift. You simply drop files into pre-configured locations on Amazon S3, and this function automatically loads into your Amazon Redshift clusters.

For automated delivery of streaming data to S3 and into to Redshift, please consider using Amazon Kinesis Firehose instead of this function.

Table of Contents

• Using AWS Lambda with Amazon Redshift
• Installing with AWS CloudFormation (Recommended)
• Installing Manually
  • Before you deploy - Lambda Execution Role
  • Deploy the function
  • Lambda Function Versions
• Configuring your VPC for connections between AWS Lambda and Redshift
• Entering the Configuration
  • The S3 Prefix
  • Hive Partitioning Style Wildcards
  • Prefix Matching
• Security
• Loading multiple Redshift clusters concurrently
• Support for Notifications & Complex Workflows
• Operations Guide
  • Viewing Previous Batches & Status
  • Working with Processed Files
  • Reprocessing a Batch
  • Unlocking a Batch
  • Deleting Old Batches
  • Reviewing Logs
  • Changing your stored Database Password or S3 Secret Key Information
  • Ensuring Loads happen every N minutes
    • Using Scheduled Lambda Functions
    • Through a CRON Job
• Extending and Building New Features
• Configuration Reference

Using AWS Lambda with Amazon Redshift

Amazon Redshift is a fully managed petabyte scale data warehouse available for less than $1000/TB/YR that provides AWS customers with an extremely powerful way to analyse their applications and business as a whole. To load their Clusters, customers ingest data from a large number of sources, whether they are FTP locations managed by third parties, or internal applications generating load files. Best practice for loading Amazon Redshift is to use the COPY command (http://docs.aws.amazon.com/redshift/latest/dg/r_COPY.html), which loads data in parallel from Amazon S3, Amazon DynamoDB or an HDFS file system on Amazon Elastic MapReduce (EMR).

Whatever the input, customers must run servers that look for new data on the file system, and manage the workflow of loading new data and dealing with any issues that might arise. That's why we created this AWS Lambda-based Amazon Redshift loader. It offers you the ability drop files into S3 and load them into any number of database tables in multiple Amazon Redshift Clusters automatically - with no servers to maintain. This is possible because AWS Lambda (http://aws.amazon.com/lambda) provides an event-driven, zero-administration compute service. It allows developers to create applications that are automatically hosted and scaled, while providing you with a fine-grained pricing structure.

The function maintains a list of all the files to be loaded from S3 into Amazon Redshift using a DynamoDB table. This list allows us to confirm that a file is loaded only one time, and allows you to determine when a file was loaded and into which table. Input file locations are buffered up to a specified batch size that you control, or you can specify a time-based threshold which triggers a load.

You can specify any of the many COPY options available, and we support loading CSV files (of any delimiter), AVRO files, as well as JSON files (with or without JSON paths specifications). All Passwords and Access Keys are encrypted for security. With AWS Lambda you get automatic scaling, high availability, and built in Amazon CloudWatch Logging.

Finally, we've provided tools to manage the status of your load processes, with built in configuration management and the ability to monitor batch status and troubleshoot issues. We also support sending notifications of load status through Simple Notification Service - SNS (http://aws.amazon.com/sns), so you have visibility into how your loads are progressing over time.

Installing with AWS CloudFormation (Recommended)

This repository includes two CloudFormation templates (deploy.yaml, and deploy-vpc.yaml) which will create much of what is needed to set up the autoloader. This section details the setup and use of the template.

This is a visual architecture of the CloudFormation installer:

The intent of this template is to simplify the setup work necessary to configure the autoloader.

Pre-work

Set up the KMS key to be used by the setup script which encrypts and decrypts the RedShift password. This key will require a specific alias, which is how the setup script picks it up. The alias must be LambdaRedshiftLoaderKey.

Also, a user will be required with the necessary privileges to run the template. This user will require an access key, which is one of the input parameters required at runtime.

The template requires several input parameters: KMS Key ARN which will be granted access from above, and if you are in VPC the Subnet ID's in which the function should egress network traffic, and which Security Groups should be granted to the function.

Usage Steps

Create a CloudFormation stack with template that you require, based upon whether you run in VPC or not. YOu can also use the table of links below. This stack will include everything needed to set up the autoloader with two exceptions. The KMS key must be created and managed separately, and a RedShift cluster will be required when setting up the autoloader. Note that this stack does not configure the autoloader, which must be done on your workstation or with an EC2 instance.

Notes

1. This stack will be created in the same region where you invoke the template.
2. The input parameters are not cross-checked at template creation time, so make sure that the subnet choice matches the availability zone you require.
3. The stack creates the Lambda trigger as well as the execution role - so they will be managed as part of the stack. It is expected that the EC2 instance and setup role can be used on an ongoing basis for the administration of the autoloader.

Launch Links

Region	Launch without VPC	Launch in VPC
eu-north-1
ap-south-1
eu-west-3
eu-west-2
eu-west-1
ap-northeast-3
ap-northeast-2
ap-northeast-1
sa-east-1
ca-central-1
ap-southeast-1
ap-southeast-2
eu-central-1
us-east-1
us-east-2
us-west-1
us-west-2

Post Install

If you prefer to use an EC2 instance to configure the database loader rather than your laptop, then run this template using CloudFormation.

Once launched, log in to the EC2 instance created as part of the stack. It contains all the necessary components set up the autoloader. 3. Invoke the setup.js script on the created EC2 instance to begin configuring the autoloader.

Function Configuration

You can set the logging behaviour of the function by adding environment variables:

• DEBUG - Sets log level debug and all messages will be shown in CloudWatch Logs
• LOG_LEVEL - Sets the log level to whatever you require, per Winston documentation

You can also set the following environment variables to change the behaviour of the loader:

• SuppressFailureStatusOnSuccessfulNotification - Allows the loader to silently fail the execution state if failure notifications were successfully sent to SNS. When set to true, you will not see failed Lambda events in CloudWatch.
• SuppressWildcardExpansionPrefixList - Allows you to specify a set of S3 Prefixes for which Hive Partitioning Wildcard expansion will not be used. Use this setting if you have prefixes which include the = symbol but which should not be converted to value=*. Requires version 2.8.0+.

Installing Manually

This option is not recommended, but can be used if you just want to patch the binaries, etc. Proceed with caution.

Before you deploy - Lambda Execution Role

You need to create an IAM policy that AWS Lambda uses when it runs, and allows it to call SNS, use DynamoDB, write Manifest files to S3, perform encryption with the AWS Key Management Service, and pass STS temporary credentials to Redshift for the COPY command. We recommend that you obtain the required permissions from the contents of the deploy.yaml as they will always be up to date.

Deploy the function

1. Go to the AWS Lambda Console in the same region as your S3 bucket and Amazon Redshift cluster.
2. Select Create a Lambda function and select the 'Author from Scratch' option
3. Enter the function name LambdaRedshiftLoader, and the Runtime value as 'Node.js '. The function name must be LambdaRedshiftLoader in order to use automated event source routing. The function was built and comprehensively tested on Node version .10, and is used by customers on a variety of other versions. Please report any issues around Node.js engine compatibility via Issues.
4. Choose the IAM role that you would like to run the Lambda function under, as configured above
5. Choose 'Create Function'
6. Under the 'Function code' section, choose 'Upload a file from Amazon S3', and use the table below to find the correct s3 location for your region.
7. Configure the default values of index.js for the filename and handler for the handler. We also recommend using the max timeout for the function to accomodate longer COPY times.

When you're done, you'll see that the AWS Lambda function is deployed and you can submit test events and view the CloudWatch Logging log streams.

Lambda Function Versions

We previously released version 1.0 in distribution AWSLambdaRedshiftLoader.zip, which didn't use the Amazon Key Management Service for encryption. If you've previously deployed and used version 1.0 and want to upgrade to version 1.1, then you'll need to recreate your configuration by running node setup.js and reentering the previous values including connection password, symmetric encryption key, and optionally an S3 Secret Key. You'll also need to upgrade the IAM policy for the Lambda Execution Role as listed below, as it now requires permissions to talk to the Key Management Service.

Furthermore, version 2.0.0 adds support for loading multiple Redshift clusters in parallel. You can deploy the 2.x versions with a 1.1x configuration, and the Lambda function will transparently upgrade your configuration to a 2.x compatible format. This uses a loadClusters List type in DynamoDB to track all clusters to be loaded.

Configuring your VPC for connections between AWS Lambda and Redshift

Please click here for a full guide on how to configure AWS Lambda to connect to Redshift in VPC and non-VPC networking environments.

Entering the Configuration

Now that your function is deployed, we need to create a configuration which tells it how and if files should be loaded from S3. Simply install AWS SDK for Javascript and configure it with credentials as outlined at http://docs.aws.amazon.com/AWSJavaScriptSDK/guide/node-intro.html and http://docs.aws.amazon.com/AWSJavaScriptSDK/guide/node-configuring.html. You'll also need a local instance of Node.js - today the included Client Tools such as setup.js only run under pre-ES6 versions of Node (0.10 and 0.12 have been tested). NVM (https://github.com/creationix/nvm/blob/master/README.markdown) is a simple way to install and switch between node versions. Then install dependencies using the following command:

cd aws-lambda-redshift-loader && npm install

In order to ensure communication with the correct AWS Region, you'll need to set an environment variable AWS_REGION to the desired location. For example, for US East use us-east-1, and for Dublin use eu-west-1.

export AWS_REGION=eu-central-1

Next, run the setup.js script by entering node setup.js. The script asks questions about how the load should be done, including those outlined in the setup appendix as the end of this document. If you are the sort of person who would rather automate this, then you can call the setup module directly by including common.js and calling function setup(useConfig, dynamoDB, s3, lambda, callback) where useConfig is a valid configuration in the form of a DynamoDB Item, and dynamoDB, S3 and lambda are all client connections to the respective services.

Alternatively, you can populate config.json with your configuration values and run node setup-file.js to run a setup script that uses a JSON configuration file instead of reading the values from the command line.

All data used to manage the lifecycle of data loads is stored in DynamoDB, and the setup script automatically provisions the following tables:

• LambdaRedshiftBatchLoadConfig - Stores the configuration of how files in an S3 input prefix should be loaded into Amazon Redshift.
• LambdaRedshiftBatches - Stores the list of all historical and open batches that have been created. There will always be one open batch, and may be multiple closed batches per S3 input prefix from LambdaRedshiftBatchLoadConfig.
• LambdaRedshiftProcessedFiles - Stores the list of all files entered into a batch, which is also used for deduplication of input files.

Once the tables are configured, the setup script will automatically create an event source for the prefix you specified in S3, and start pushing ObjectCreated:* events to the database loader.

*** IMPORTANT ***

The tables used by this function are created with BillingMode: "PAY_PER_REQUEST" (from version 2.6.8), which means that they will autoscale as needed to cope with the demand placed on them by the loader, which is a function of the frequency that files land in S3. If you have extremely peaky workloads where thousands of files arrive at the same time, but infrequently, then you may see Provisioned Throughput based Throttling of the function, and we would advise you move to Provisioned IO to meet your Peak Read/Write capacity requirements.

The S3 Prefix

When you enter the configuration, you must provide an S3 Prefix. This is used by the function to resolve which configuration to use for an incoming event. There are two dimensions of dynamic resolution provided which will help you respond to events where the path is variable over time or from provider:

Hive Partitioning Style Wildcards

You may have implemented a great practice of segregating S3 objects using time oriented buckets. Data for January 2016 sits in a prefix mybucket/data/<type>/2016/01 while data for Feburary is in mybucket/data/<type>/2016/02. Rather than having to create one configuration per year and month, you can instead use Hive Partitioning style prefixes. If you place S3 objects into a prefix mybucket/data/<type>/yyyy=2016/dd=01, you can then create a configuration with an S3 prefix mybucket/data/<type>/yyyy=*/dd=*. The incoming event will be pre-processed and files which use this convention will always match the wildcard configuration.

Prefix Matching

In some cases, you may want to have a configuration for most parts of a prefix, but a special configuration for just a subset of the data within a Prefix. In addition to Hive partitioning style wildcards, you can also create configuration hierarchies. In the above example, if I wanted to process data from 2016 with a single configuration, but had special rules for February only, then you can create 2 configurations:

• mybucket/data/<type>/2016 This will match anything that is submitted for 2016, regardless of other information provided.
• mybucket/data/<type>/2016/02 This will only match input events that were submitted for February 2016, and give you the ability to provide a new configuration item. Some examples of matching are included below:

Security

When the Redshift COPY command is created, by default the Lambda function will use a temporary STS token as credentials for Redshift to use when accessing S3. You can also optionally configure an Access Key and Secret Key which will be used instead, and the setup utility will encrypt the secret key.

Redshift supports two options for connecting to the cluster: IAM Role based authentication, and Username/Password based authentication. While we highly recommend using IAM Role based authentication, it is not avialable with this utility as it requires the use of the Redshift JDBC Driver, which we don't yet support in this module.

The database password, as well as the master symmetric key used for encryption operations, must be encrypted by the Amazon Key Management Service before running the setup utility. To perform this encryption, you will use the encryptValue.js script supplied with this project. The encryption is done with a KMS Customer Master Key with an alias named alias/LambdaRedshiftLoaderKey. If you supply an unencrypted password to the setup scripts, they will store the value in Dynamodb (BAD), but these will not be usable by the system as it will throw a decryption error on execution.

If you would like support for storage of passwords in the SSM Parameter Store, please +1 this issue.

Loading multiple Redshift clusters concurrently

Version 2.0.0 and higher adds the ability to load multiple clusters at the same time. To configure an additional cluster, you must first have deployed version AWSLambdaRedshiftLoader-2.1.0.zip or higher and had your configuration upgraded to 2.x format (you will see a new loadClusters List type in your configuration). You can then use the addAdditionalClusterEndpoint.js to add new clusters into a single configuration. This will require you enter the vital details for the cluster including endpoint address and port, DB name and password.

You are now ready to go. Simply place files that meet the configured format into S3 at the location that you configured as the input location, and watch as AWS Lambda loads them into your Amazon Redshift Cluster. You are charged by the number of input files that are processed, plus a small charge for DynamoDB. You now have a highly available load framework which doesn't require you manage servers!

Support for Notifications & Complex Workflows

There are two options for creating complex ELT type data flows with the Lambda Loader. The first is to use pre and post-sql commands to prepare, load, and then react to new data being loaded into a table. This allows you to set the presql and postsql portions of the configuration to add SQL statements to manipulate the database as needed, and these statements are run transactionally with the COPY command.

In addition to pre/post-sql, this function can send notifications on completion of batch processing. Using SNS, you can then receive notifications through email and HTTP Push to an application, or put them into a queue for later processing. You can even invoke additional Lambda functions to complete your data load workflow using an SNS Event Sources for another AWS Lambda function. If you would like to receive SNS notifications for succeeded loads, failed loads, or both, create SNS Topics and take note of their ID's in the form of Amazon Resource Notations (ARN) for later use in the configuration setup. An example failure notification message:

{
  "batchId": "2790a034-4954-47a9-8c53-624575afd83d",
  "error": "{\"localhost\":{\"status\":-1,\"error\":{\"code\":\"ECONNREFUSED\",\"errno\":\"ECONNREFUSED\",\"syscall\":\"connect\",\"address\":\"127.0.0.1\",\"port\":5439}}}",
  "failedManifest": "meyersi-ire/redshift/manifest/failed/manifest-2018-04-26 10:34:02-5230",
  "key": "input/redshift-input-0.csv",
  "originalManifest": "meyersi-ire/redshift/manifest/manifest-2018-04-26 10:34:02-5230",
  "s3Prefix": "lambda-redshift-loader/input",
  "status": "error"
}

The loader supports the ability to suppress a failure status of the Lambda function if you have configured a failure notification SNS topic, which should only be used if you know that you've created a downstream workflow to deal with these notifications completely, and you do not want Lambda level failures to be exposed. Please note that this error suppression is only available for batch level load failures, and not for other types of failures of the function (for example if it's unable to complete a status update or send notifications at all). This suppression is only available if you've configured a failureTopicARN in your S3 prefix configuration.

To suppress Lambda level failures, set environment variable SuppressFailureStatusOnSuccessfulNotification = 'true' in your Lambda configuration.

Operations Guide

Viewing Previous Batches & Status

If you ever need to see what happened to batch loads into your Cluster, you can use the 'queryBatches.js' script to look into the LambdaRedshiftBatches DynamoDB table. It can be called by:

node queryBatches.js --region <region> --batchStatus <batchStatus> --startDate <beginning of date range> --endDate <end of date range>

• region - the region in which the AWS Lambda function is deployed
• batchStatus - the batch status you are querying for, including 'error', 'complete', 'pending', or 'locked'
• startDate - optional date argument to use as a start date for querying batches
• endDate - optional date argument to use as an end date for the query window

Running node queryBatches.js --region eu-west-1 --batchStatus error might return a list of all batches with a status of 'error' in the EU (Ireland) region, such as:

[
    {
        "s3Prefix": "lambda-redshift-loader-test/input",
        "batchId": "2588cc35-b52f-4408-af89-19e53f4acc11",
        "lastUpdateDate": "2015-02-26-16:50:18"
    },
    {
        "s3Prefix": "lambda-redshift-loader-test/input",
        "batchId": "2940888d-146c-47ff-809c-f5fa5d093814",
        "lastUpdateDate": "2015-02-26-16:50:18"
    }
]

If you require more detail on a specific batch, you can use describeBatch.js to show all detail for a batch. It takes 3 arguments as well:

• region - the region in which the AWS Lambda function is deployed
• batchId - the batch you would like to see the detail for
• s3Prefix - the S3 Prefix the batch was created for

Which would return the batch information as it is stored in Dynamo DB:

{
    "batchId": {
        "S": "7325a064-f67e-416a-acca-17965bea9807"
    },
    "manifestFile": {
        "S": "my-bucket/manifest/manifest-2015-02-06-16:20:20-2081"
    },
    "s3Prefix": {
        "S": "input"
    },
    "entries": {
        "L": [
            {"file":"input/sample-redshift-file-for-lambda-loader.csv", "size": N},
            "input/sample-redshift-file-for-lambda-loader1.csv",
            "input/sample-redshift-file-for-lambda-loader2.csv",
            "input/sample-redshift-file-for-lambda-loader3.csv",
            "input/sample-redshift-file-for-lambda-loader4.csv",
            "input/sample-redshift-file-for-lambda-loader5.csv"
        ]
    },
    "lastUpdate": {
        "N": "1423239626.707"
    },
    "status": {
        "S": "complete"
    }
}

Working with Processed Files

We'll only load a file once by default, but in certain rare cases you might want to re-process a file, such as if a batch goes into error state for some reason. If so, use the processedFiles.js script to query or delete processed files entries. The script takes an operation type and filename as arguments:

• Use --query to query if a file has been processed at all, and if so by which batch.
• Use --delete to delete a given file entry.
• Use --reprocess for files that couldn't be added to a batch for some reason, which might include DynamoDB throttling. This will perform an in-place file copy on S3, which will then be received by the Lambda loader and it will attempt to reprocess the file. If it's already part of a batch, then it will be ignored. Please note this option is only supported by version 2.5.5 and higher.

An example of the processed files store can be seen below:

Reprocessing a Batch

If you ever need to reprocess a batch - for example if it failed to load the required files for some reason - then you can use the reprocessBatch.js script. This takes the same arguments as describeBatch.js (region, batch ID & input location). The original input batch is not affected; instead, each of the input files that were part of the batch are removed from the LambdaRedshiftProcessedFiles table, and then the script forces an S3 event to be generated for the file. This will be captured and reprocessed by the function as it was originally. Please note you can only reprocess batches that are not in 'open' status. Please also note that because this function reads and then re-writes object metadata, it is potentially liable to overwriting metadata added by a different process. If you have frequent S3 metadata re-write as part of your application, use with extreme caution.

Unlocking a Batch

It is possible, but rare, that a batch would become locked but not be being processed by AWS Lambda. If this were to happen, please use unlockBatch.js including the region and Batch ID to set the batch to 'open' state again.

Deleting Old Batches

As the system runs for some time, you may find that your LambdaRedshiftBatches table grows to be very large. In this case, you may want to archive old Completed batches that you no longer require.

USE THIS FEATURE WITH CAUTION!!! IT WILL DELETE DATA!!!

If you would like to clear out old delete batch entries, then you can use the deleteBatches.js script. It will allow you to query for batches that are 'complete' and then clear them out of the system. It does not currently support deleting other types of batches (error, locked, pending), as these should be reprocessed or would make no sense to delete. To run the script, execute:

deleteBatches.js --region <region> --batchStatus <batchStatus> --startDate <beginning of date range> --endDate <end of date range>

This function will return console output (and can also be used programmatically) and for example, when specified as above will show:

node deleteBatches.js --region eu-west-1 --batchStatus error
Dry run only - no batches will be modified
Resolved 1 Batches for Deletion
OK: Deletion of 0 Batches
Deleted Batch Information:
{ s3Prefix: 'lambda-redshift-loader-test/input',
  batchId: '43643fda-f829-4f60-820a-2ce331e62b18',
  status: 'complete',
  lastUpdateDate: '2016-03-10-10:33:12',
  lastUpdate: '1457605992.447' }

This will allow you test your batch deletion and understand the impact of performing such a change. When you are completely happy to delete batches as outlined in the dry run, then add --dryRun to the command line, or supply false for the dryRun parameter. This will ACTUALLY REALLY DELETE BATCH INFORMATION. To mitigate risk of data loss in error, the return of this function is an array of all the batch information that was deleted, so that you can save logfiles for future recovery if needed. For example:

node deleteBatches.js --region eu-west-1 --batchStatus error --endDate 1457434179 --dryRun false
Deleting 1 Batches in status error
OK: Deletion of 1 Batches
Deleted Batch Information:
{
  "batchId": {
    "S": "fe5876bc-9eeb-494c-a66d-ada4698f4405"
  },
  "clusterLoadStatus": {
    "S": {
      "db1.cluster.eu-west-1.redshift.amazonaws.com": {
        "error": {
          "code": "ETIMEDOUT",
          "errno": "ETIMEDOUT",
          "syscall": "connect"
        },
        "status": -1
      },
      "db2.cluster.amazonaws.com": {
        "error": {
          "code": "ENOTFOUND",
          "errno": "ENOTFOUND",
          "syscall": "getaddrinfo"
        },
        "status": -1
      }
    }
  },
  "entries": {
    "SS": [
      "lambda-redshift-loader-test/input/redshift-input-0.csv",
      "lambda-redshift-loader-test/input/redshift-input-2.csv"
    ]
  },
  "errorMessage": {
    "S": {
      "db1.cluster.eu-west-1.redshift.amazonaws.com": {
        "error": {
          "code": "ETIMEDOUT",
          "errno": "ETIMEDOUT",
          "syscall": "connect"
        },
        "status": -1
      },
      "db2.cluster.eu-west-1.redshift.amazonaws.com": {
        "error": {
          "code": "ENOTFOUND",
          "errno": "ENOTFOUND",
          "syscall": "getaddrinfo"
        },
        "status": -1
      }
    }
  },
  "lastUpdate": {
    "N": "1457434178.86"
  },
  "lastUpdateDate": "2016-03-08-10:49:38",
  "manifestFile": {
    "S": "my-bucket/lambda/redshift/failed/manifest-2016-03-08-10:47:30-1368"
  },
  "s3Prefix": {
    "S": "lambda-redshift-loader-test/input"
  },
  "status": {
    "S": "error"
  },
  "writeDates": {
    "NS": [
      "1457434049.802",
      "1457433786.56"
    ]
  }
}

As you can see the entire contents of the batch are returned to you, so that you can ensure no possiblity of data loss. The most important features of this returned data structure are likely $entries.SS, $manifestFile.S, which would allow you to re-inject files into the loader if needed.

Reviewing Logs

For normal operation, you won't have to do anything from an administration perspective. Files placed into the configured S3 locations will be loaded when the number of new files equals the configured batch size. You may want to create an operational process to deal with failure notifications, but you can also just view the performance of your loader by looking at Amazon CloudWatch. Open the CloudWatch console, and then click 'Logs' in the lefthand navigation pane. You can then select the log group for your function, with a name such as /aws/lambda/<My Function>.

Each of the above Log Streams were created by an AWS Lambda function invocation, and will be rotated periodically. You can see the last ingestion time, which is when AWS Lambda last pushed events into CloudWatch Logging.

You can then review each log stream, and see events where your function simply buffered a file, or where it performed a load.

Changing your stored Database Password or S3 Secret Key Information

Currently you must edit the configuration manually in Dynamo DB to make changes. If you need to update your Redshift DB Password, or your Secret Key for allowing Redshift to access S3, then you can use the encryptValue.js script to encrypt a value using the Lambda Redshift Loader master key and encryption context.

To run:

node encryptValue.js --region <region> --input <Value to Encrypt>

This script encrypts the value with Amazon KMS, and then verifies the encryption is correct before returning a JSON object which includes the input value and the encrypted Ciphertext. You can use the 'encryptedCiphertext' attribute of this object to update the Dynamo DB Configuration.

Ensuring Loads happen every N minutes

If you have a prefix that doesn't receive files very often, and want to ensure that files are loaded every N seconds, use the following process to force periodic loads.

When you create the configuration, specify a batchTimeoutSecs and add a filenameFilterRegex such as '.*.csv' (which only loads CSV files that are put into the specified S3 prefix). Then every N seconds, schedule one of the included trigger file generators to run:

Using Scheduled Lambda Functions

You can use an included Lambda function to generate trigger files into all configured prefixes that have a regular expression filter, by completing the following:

• Create a new AWS Lambda Function, and deploy the same zip file from the dist folder as you did for the AWS Lambda Redshift Loader. However, when you configure the Handler name, use createS3TriggerFile.handler, and configure it with the timeout and RAM required.
• In the AWS Web Console, select Services/CloudWatch, and in the left hand navigation select 'Events/Rules'
• Choose Event Source = 'Schedule' and specify the interval for your trigger files to be gnerated
• Add Target to be the Lambda function you previously configured

Once done, you will see CloudWatch Logs being created on the configured schedule, and trigger files arriving in the specified prefixes

Through a CRON Job

You can use a Python based script to generate trigger files to specific input buckets and prefixes, using the following utility:

./path/to/function/dir/generate-trigger-file.py <region> <input bucket> <input prefix> <local working directory>

• region - the region in which the input bucket for loads resides
• input bucket - the bucket which is configured as an input location
• input prefix - the prefix which is configured as an input location
• local working directory - the location where the stub dummy file will be kept prior to upload into S3

These methods write a file called 'lambda-redshift-trigger-file.dummy' to the configured input prefix, which causes your deployed function to scan the open pending batch and load the contents if the timeout seconds limit has been reached. The batch timeout is calculated on the basis of when the first file was added to the batch.

Extending and Building New Features

We're excited to offer this AWS Lambda function under the Amazon Software License. The GitHub repository does not include all the dependencies for Node.js, so in order to build and run locally please install the following modules with npm install:

• Node Postgres - Native Postgres Driver for Javascript (https://github.com/brianc/node-postgres & npm install pg)
• Async - Higher-order functions and common patterns for asynchronous code (https://www.npmjs.com/package/async & npm install async)
• Node UUID - Rigorous implementation of RFC4122 (v1 and v4) UUIDs (https://www.npmjs.com/package/node-uuid & npm install node-uuid)

Configuration Reference

The following section provides guidance on the configuration options supported. For items such as the batch size, please keep in mind that in Preview the Lambda function timeout is 60 seconds. This means that your COPY command must complete in less than ~ 50 seconds so that the Lambda function has time to complete writing batch metadata. The COPY time will be a function of file size, the number of files to be loaded, the size of the cluster, and how many other processes might be consuming WorkLoadManagement queue slots.

An example configuration looks like this:

{
  "batchSize": 100,
  "batchTimeoutSecs": 100,
  "copyOptions": "FILLRECORD EMPTYASNULL BLANKSASNULL ",
  "csvDelimiter": "|",
  "currentBatch": "1fafd16b-6e44-458f-8222-db8f74f9aac4",
  "dataFormat": "CSV",
  "failedManifestKey": "redshift/manifest/failed",
  "failureTopicARN": "arn:aws:sns:eu-west-1:123456789:LambdaRedshiftLoadErrors",
  "lastBatchRotation": "2018-11-15 18:22:43",
  "lastUpdate": 1489594870.252,
  "loadClusters": [
    {
      "clusterDB": "master",
      "clusterEndpoint": "mycluster.xxxxxx-west-1.redshift.amazonaws.com",
      "clusterPort": 5439,
      "connectPassword": "AQECAHh+YtzV/K7+L/VDT7h2rYDCWFSUugXGqMxzWGXynPCHpQAAAGkwZwYJKoZIhvcNAQcGoFowWAIBADBTBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDGfZWdg/pXqRzMPlQAIBEIAmF4Xe+Hcy53+LM2/OGu04RySGIZ4pny12Krks/EblJhjlIVv3JIM=",
      "connectUser": "master",
      "targetTable": "test_table",
      "truncateTarget": false,
      "useSSL": true
    }
  ],
  "manifestBucket": "mybucket",
  "manifestKey": "redshift/manifest",
  "s3Prefix": "lambda-redshift-loader-test/input",
  "status": "open",
  "version": "2.6.2"
}

Release Notes

Version 2.7.9: Fixed a bug in how the content length was specified in the manifest file. Now, file size is captured on each entry into the pending batch, and this information is used to generate the correct content length.

Copyright 2014-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.

Licensed under the Amazon Software License (the "License"). You may not use this file except in compliance with the License. A copy of the License is located at

http://aws.amazon.com/asl/

or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions and limitations under the License.