#LOCAL DYNAMODB METRICS SERIAL#

Multiple serial requests donвЂ™t scale and, as you scale, your application gets slower. It obviously can impact the performance and often it cannot be mitigated with parallel calls. The most important and the biggest cost you pay with the multi-table design is the need for multiple network trips to fetch joined entities.

multiple network trips/requests to fetch joined entities.However, you could mitigate it by using the On-demand capacity mode where you вЂњdonвЂ™t seeвЂќ WCUs and RCUs anymore. operational costs (you need to provision at least 1 WCU and 1 RCU to every table, even to some dictionary-like, infrequently used ones).operational overheads - many tables to manage and maintain (metrics, monitoring, alerting).

This approach incurs several costs at scale, i.e. However, non-existing table joins in DynamoDB would have to be translated into multiple requests to the database. Translating a RDBMS model to NoSQL without changes would also result in many tables. think about Postgresql with Json data types). entity per table and some degree of normalization (the schemaless nature of DynamoDB and support for complex attributes gives you more options to express 1-to-* relationships, e.g. The most straightforward way to design your DynamoDB data model is to follow the best practices used in the RDBMS world, i.e. A multi-table design which resembles RDBMS modelling (just with more constraints) which stores entities in separate tables, or a single table design where all entities are stored in one common table. Modeling a DynamoDB database can take one of these two distinct approaches. all, include, keys only, for secondary indexes not included in the picture). In summary, DynamoDBвЂ™s table structure looks as follows (different attribute projection types, i.e. YouвЂ™ve got two options - a Local Secondary Index (LSI) and a Global Secondary Index (GSI). In addition to the base table, you can set up additional secondary indexes to increase queries performance. And it allows selective patterns and range queries. It is heavily used when modelling 1:N relationships and hierarchies.

partition key - determines data division and allows simple key-value access patterns.

In a nutshell, the primary keyвЂ™s components play the following roles DynamoDB uses partition keys, as the name implies, to partition your data physically and to get the best performance it should spread workloads among all/most partitions. You should avoid вЂњhot partitionsвЂќ, the partitions that are accessed overwhelmingly frequently. values should be distinct in respect to the number of rows and ideally should be accessed uniformly. Whichever primary key scheme you use, itвЂ™s very important to select a good partition key, i.e. As we will see further, sort keys, besides the obvious sorting capability, play an important role in making your data queries more flexible.

partition key + sort key - a composite primary key which consists of a partition key and an additional sort key which can sort items within the given partition.

partition key - a single, simple value attribute.

The only mandatory attribute in an item is a primary key which comes in two flavours a map or list (think of columns or fields in other db systems). a number, a string, or a complex object, e.g.

Item - an item is an identifiable group of attributes (think of rows or records in other db systems).ДЄttribute - an attribute is a fundamental, named block of data. However, apart from the mandatory primary key, it is basically schemaless and in the same table you can store a collection of heterogeneous items. Table - DynamoDB borrows the nomenclature from RDBMS and, similarly to other database systems, stores data in tables. To better understand why that is (keeping few tables, ideally only one) and why it might be beneficial, let's briefly review the DynamoDB data model. AWS DynamoDB Docs ( here) repeatedly stateДЄs a general rule, you should maintain as few tables as possible in a DynamoDB application. One thing that stands out in DynamoDB is the focus on keeping as few tables as possible. In this respect, DynamoDB is not different from other NoSQL databases, where generally you need to вЂњforgetвЂќ your RDBMS training (e.g. However, to achieve this kind of performance, for non-trivial use cases, with huge scale and traffic you need to model your data carefully. DynamoDB is a fully-managed NoSQL key-value database which delivers single-digit performance at any scale.

Local dynamodb metrics