FSharp.AWS.DynamoDB is an F# wrapper over the standard AWSSDK.DynamoDBv2 library that represents Table Items as F# records, enabling one to perform updates, queries and scans using F# quotation expressions.

The API draws heavily on the corresponding FSharp.Azure.Storage wrapper for Azure table storage.

Table items can be represented using F# records:

open FSharp.AWS.DynamoDB

type WorkItemInfo =
    {
        [<HashKey>]
        ProcessId : int64
        [<RangeKey>]
        WorkItemId : int64

        Name : string
        UUID : Guid
        Dependencies : Set<string>
        Started : DateTimeOffset option
    }

We can now perform table operations on DynamoDB like so:

open Amazon.DynamoDBv2
open FSharp.AWS.DynamoDB.Scripting // Expose non-Async methods, e.g. PutItem/GetItem

let client : IAmazonDynamoDB = ``your DynamoDB client instance``
let table = TableContext.Initialize<WorkItemInfo>(client, tableName = "workItems", Throughput.OnDemand)

let workItem = { ProcessId = 0L; WorkItemId = 1L; Name = "Test"; UUID = guid(); Dependencies = set [ "mscorlib" ]; Started = None; SubProcesses = [ "one"; "two" ] }

let key : TableKey = table.PutItem(workItem)
let workItem' = table.GetItem(key)

Queries and scans can be performed using quoted predicates:

let qResults = table.Query(keyCondition = <@ fun r -> r.ProcessId = 0 @>, 
                           filterCondition = <@ fun r -> r.Name = "test" @>)
                            
let sResults = table.Scan <@ fun r -> r.Started.Value >= DateTimeOffset.Now - TimeSpan.FromMinutes 1.  @>

Values can be updated using quoted update expressions:

let updated = table.UpdateItem(<@ fun r -> { r with Started = Some DateTimeOffset.Now } @>, 
                               preCondition = <@ fun r -> r.DateTimeOffset = None @>)

Or they can be updated using the SET, ADD, REMOVE and DELETE operations of the UpdateOp` DSL, which is closer to the underlying DynamoDB API:

let updated = table.UpdateItem <@ fun r -> SET r.Name "newName" &&& ADD r.Dependencies ["MBrace.Core.dll"] @>

Preconditions that are not upheld are signalled via an Exception by the underlying AWS SDK. These can be trapped using the supplied exception filter:

try let! updated = table.UpdateItemAsync(<@ fun r -> { r with Started = Some DateTimeOffset.Now } @>,
                                         preCondition = <@ fun r -> r.DateTimeOffset = None @>)
    return Some updated
with Precondition.CheckFailed ->
    return None 

FSharp.AWS.DynamoDB supports the following field types:

• Numerical types, enumerations and strings.
• Array, Nullable, Guid, DateTimeOffset and TimeSpan.
• F# lists
• F# sets with elements of type number, string or byte[].
• F# maps with key of type string.
• F# records and unions (recursive types not supported, nested ones are).

Query expressions support the following F# operators in their predicates:

• Array.length, List.length, Set.count and Map.Count.
• String.StartsWith and String.Contains.
• Set.contains and Map.containsKey NOTE: Only works for checking if a single value is contained in a set in the table. eg: Valid:table.Query(<@ fun r -> r.Dependencies |> Set.contains "mscorlib" @>) Invalid table.Query(<@ fun r -> set ["Test";"Other"] |> Set.contains r.Name @>)
• Array.contains,List.contains
• Array.isEmpty and List.isEmpty.
• Option.isSome, Option.isNone, Option.Value and Option.get.
• fst and snd for tuple records.

Update expressions support the following F# value constructors:

• (+) and (-) in numerical and set types.
• Array.append and List.append (or @).
• List consing (::).
• defaultArg on optional fields.
• Set.add and Set.remove.
• Map.add and Map.remove.
• Option.Value and Option.get.
• fst and snd for tuple records.

type private CounterEntry = { [<HashKey>] Id : Guid ; Value : int64 }

type Counter private (table : TableContext<CounterEntry>, key : TableKey) =
    
    member _.Value = async {
        let! current = table.GetItemAsync(key)
        return current.Value
    }
    
    member _.Incr() = async { 
        let! updated = table.UpdateItemAsync(key, <@ fun e -> { e with Value = e.Value + 1L } @>)
        return updated.Value
    }

    static member Create(client : IAmazonDynamoDB, tableName : string) = async {
        let table = TableContext<CounterEntry>(client, tableName)
        let throughput = ProvisionedThroughput(readCapacityUnits = 10L, writeCapacityUnits = 10L)        
        let! _desc = table.VerifyOrCreateTableAsync(Throughput.Provisioned throughput)
        let initialEntry = { Id = Guid.NewGuid() ; Value = 0L }
        let! key = table.PutItemAsync(initialEntry)
        return Counter(table, key)
    }

NOTE: It's advised to split single time initialization/verification of table creation from the application logic, see Script.fsx for further details.

Projection expressions can be used to fetch a subset of table attributes, which can be useful when performing large queries:

table.QueryProjected(<@ fun r -> r.HashKey = "Foo" @>, <@ fun r -> r.HashKey, r.Values.Nested.[0] @>)

the resulting value is a tuple of the specified attributes. Tuples can be of any arity but must contain non-conflicting document paths.

Global Secondary Indices can be defined using the GlobalSecondaryHashKey and GlobalSecondaryRangeKey attributes:

type Record =
    {
        [<HashKey>] HashKey : string
        ...
        [<GlobalSecondaryHashKey(indexName = "Index")>] GSIH : string
        [<GlobalSecondaryRangeKey(indexName = "Index")>] GSIR : string
    }

Queries can now be performed on the GSIH and GSIR fields as if they were regular HashKey and RangeKey Attributes.

NOTE: Global secondary indices are created using the same provisioned throughput as for the primary keys.

Local Secondary Indices can be defined using the LocalSecondaryIndex attribute:

type Record =
    {
        [<HashKey>] HashKey : string
        [<RangeKey>] RangeKey : Guid
        ...
        [<LocalSecondaryIndex>] LSI : double
    }

Queries can now be performed using LSI as a secondary RangeKey.

NB: Due to API restrictions, the secondary index support in FSharp.AWS.DynamoDB always projects ALL table attributes. NOTE: A key impact of this is that it induces larger write and storage costs (each write hits two copies of everything) although it does minimize read latency due to extra 'fetch' operations - see the LSI documentation for details.

Pagination is supported on both scans & queries:

let firstPage = table.ScanPaginated(limit = 100)
printfn "First 100 results = %A" firstPage.Records
match firstPage.LastEvaluatedKey with
| Some key ->
    let nextPage = table.ScanPaginated(limit = 100, exclusiveStartKey = key)

Note that the exclusiveStartKey on paginated queries must include both the table key fields and the index fields (if querying an LSI or GSI). This is accomplished via the IndexKey type - if constructing manually (eg deserialising a start key from an API call):

let startKey = IndexKey.Combined(gsiHashValue, gsiRangeValue, TableKey.Hash(primaryKey))
let page = table.QueryPaginated(<@ fun t -> t.GsiHash = gsiHashValue @>, limit = 100, exclusiveStartKey = startKey)

Due to restrictions of DynamoDB, it may sometimes be the case that objects are not persisted faithfully. For example, consider the following record definition:

type Record = 
    {         
        [<HashKey>]
        HashKey : Guid

        Optional : int option option
        Lists : int list list
    }
    
let item = { HashKey = Guid.NewGuid() ; Optional = Some None ; Lists = [[1;2];[];[3;4]] }
let key = table.PutItem item

Subsequently recovering the given key will result in the following value:

> table.GetItem key
val it : Record = {HashKey = 8d4f0678-6def-4bc9-a0ff-577a53c1337c;
                   Optional = None;
                   Lists = [[1;2]; [3;4]];}

It is possible to precompute a DynamoDB expression as follows:

let precomputedConditional = table.Template.PrecomputeConditionalExpr <@ fun w -> w.Name <> "test" && w.Dependencies.Contains "mscorlib" @>

This precomputed conditional can now be used in place of the original expression in the FSharp.AWS.DynamoDB API:

let results = table.Scan precomputedConditional

FSharp.AWS.DynamoDB also supports precomputation of parametric expressions:

let startedBefore = table.Template.PrecomputeConditionalExpr <@ fun time w -> w.StartTime.Value <= time @>
table.Scan(startedBefore (DateTimeOffset.Now - TimeSpan.FromDays 1.))

(See Script.fsx for example timings showing the relative efficiency.)

FSharp.AWS.DynamoDB supports DynamoDB transactions via the Transaction class.

The supported individual operations are:

• Check: ConditionCheck - potentially veto the batch if the (precompiled) condition is not fulfilled by the item identified by key
• Put: PutItem-equivalent operation that upserts a supplied item (with an optional precondition)
• Update: UpdateItem-equivalent operation that applies a specified updater expression to an item with a specified key (with an optional precondition)
• Delete: DeleteItem-equivalent operation that deletes the item with a specified key (with an optional precondition)

let compile = table.Template.PrecomputeConditionalExpr
let doesntExistCondition = compile <@ fun t -> NOT_EXISTS t.Value @>
let existsCondition = compile <@ fun t -> EXISTS t.Value @>
let key = TableKey.Combined(hashKey, rangeKey)

let transaction = table.CreateTransaction()

transaction.Check(table, key, doesntExistCondition)
transaction.Put(table, item2, None)
transaction.Put(table, item3, Some existsCondition)
transaction.Delete(table, table.Template.ExtractKey item5, None)

do! transaction.TransactWriteItems()

Failed preconditions (or Checks) are signalled as per the underlying API: via a TransactionCanceledException. Use Transaction.TransactionCanceledConditionalCheckFailed to trap such conditions:

try do! transaction.TransactWriteItems()
        return Some result
with Transaction.TransactionCanceledConditionalCheckFailed -> return None 

See TransactWriteItems tests for more details and examples.

It generally costs double or more the Write Capacity Units charges compared to using precondition expressions on individual operations.

Critical to any production deployment is to ensure that you have good insight into the costs your application is incurring at runtime.

A hook is provided so metrics can be published via your preferred Observability provider. For example, using Prometheus.NET:

let dbCounter = Prometheus.Metrics.CreateCounter("aws_dynamodb_requests_total", "Count of all DynamoDB requests", "table", "operation")
let processMetrics (m : RequestMetrics) =
    dbCounter.WithLabels(m.TableName, string m.Operation).Inc()
let table = TableContext<WorkItemInfo>(client, tableName = "workItems", metricsCollector = processMetrics)

If metricsCollector is supplied, the requests will set ReturnConsumedCapacity to ReturnConsumedCapacity.INDEX and the RequestMetrics parameter will contain a list of ConsumedCapacity objects returned from the DynamoDB operations.

DynamoDB follows an eventually consistent model by default. As a consequence, data returned from a read operation might not reflect the changes of the most recently performed write operation if they are made in quick succession. To circumvent this limitation and enforce strongly consistent reads, DynamoDB provides a ConsistentRead parameter for read operations. You can enable this by supplying the consistentRead parameter on the respective TableContext methods, e.g. for GetItem:

async {
    let! key : TableKey = table.PutItemAsync(workItem)
    let! workItem = table.GetItemAsync(key, consistentRead = true)
}

Note: strongly consistent reads are more likely to fail, have higher latency, and use more read capacity than eventually consistent reads.

To build using the dotnet SDK:

dotnet tool restore dotnet build

Tests are run using dynamodb-local on port 8000. Using the docker image is recommended:

docker run -p 8000:8000 amazon/dynamodb-local

then

dotnet test -c Release

• @samritchie

The default maintainer account for projects under "fsprojects" is @fsprojectsgit - F# Community Project Incubation Space (repo management)