#Boto3 local dynamodb code#
#Boto3 local dynamodb Offline#

It's powerful enough that it basically is the database behind, so I think it probably can be used for many different use cases. wait_until_exists () print ( 'Table Created!' ) returnĭynamoDB is a powerful and flexible NoSQL system - and one of the advantages of using it over MongoDB, for instance, is that you don't have to manage it. exc_info () print ( 'Dynamo Create Table Error:', error ) print ( 'Waiting for table creation.' ) new_table. create_table ( AttributeDefinitions = [, TableName = table_name, GlobalSecondaryIndexes = indexes, ) except : error = sys. Essentially telling our modules where to collect all of the information to reference, and what dynamoDB table to use. Directing our function to get the different properties our function will need to reference such as bucket name from the s3 object,etc. resource ( 'dynamodb', region_name = region_name ) throughput = int ( throughput ) if indexes : for index in indexes : index = int ( index ) index = int ( index ) if not sort_key and not indexes : new_table = dynamodb. First, we’re importing the boto3 and json Python modules. Session ( profile_name = profile ) dynamodb = session. keys (): attributedefs = kwargs else : attributedefs = '' print ( 'creating necessary table.' ) session = boto3. keys (): indexes = kwargs else : indexes = '' if 'attributedefs' in kwargs. keys (): throughput = kwargs else : throughput = '5' if 'indexes' in kwargs. keys (): sort_key = kwargs else : sort_key = '' if 'throughput' in kwargs. Let's say you want to check if a table exists, create a table, then fill it with some values from a dictionary.ĭef create_table ( ** kwargs ): ''' This creates a new table in Dynamo args: table_name, region_name, partition_key, sort_key, throughput, indexes, attributedefs ''' table_name = kwargs region_name = kwargs profile = kwargs partition_key = kwargs if 'sort_key' in kwargs.

#Boto3 local dynamodb code#

One thing I mentioned in the past post is that when you are using Boto3 heavily, it is important to build your own set of Boto3 functions which help you to make your code more readable, and so that you don't repeat yourself. You can create new tables, read and write data either individually or in bulk, you can delete tables, change table capacities, set up auto-scaling, etc. There we created a table named ‘test’ with primary key ‘date’ of type string(‘S’). Connecting to it is as easy as changing the endpoint parameter in boto3.resource call. Now, within the testwriteintotable() method, create a DynamoDB resource like following dynamodb boto3.resource('dynamodb') Let’s create a DynamoDB table using the DynamoDB resource like below-given code.

#Boto3 local dynamodb Offline#

Using Boto3, you can operate on DynamoDB stores in pretty much any way you would ever need to. If you need to use DynamoDB offline locally, you can use DynamoDB local distributed by AWS or DynamoDB from Localstack. It's a little out of the scope of this blog entry to dive into details of DynamoDB, but it has some similarities to other NoSQL database systems like MongoDB and CouchDB. One of the services I've used quite a lot is DynamoDB. As I described in my last post, Boto3 is the higher level SDK for AWS services.