GitBook: [main] 53 pages and 14 assets modified

development_development/development_overview.md

@@ -1,8 +1,6 @@
 # Overview
 
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 In the Quick Start section we incrementally created a distributed, database-backed request processing application using existing services with Aquamarine. Of course, we left a lot of detail uncovered including where the services we used came from in the first place. In this section, we tackle the very issue of development and deployment of service component.
 
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 Before we proceed, please make sure your Fluence environment is [setup](../recipes_recipes/recipes_setting_up.md) and ready to go. Moreover, we are going to run our own Fluence node to test our services in a network environment. Please refer to the [Running a Local Fluence Node](../tutorials_tutorials/tutorial_run_local_node.md) tutorial if you need support.
 

development_development/development_reward_block_app/development_additional_concepts.md

@@ -4,7 +4,6 @@ In the previous sections we obtained block reward data by discovering the latest
 
 As we have seen in our AIR workflows, particles travel the path, trigger execution, and update their data. So far, we have only seen services consume previous outputs as \(complete\) inputs, which means that service at workflow sequence s needs to be fairly tightly coupled to service at sequence s-1, which is less than ideal. Luckily, Fluence provides a solution to access certain types of results as j_son paths_.
 
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 ## Peer-Based Script Storage And Execution
 
 As discussed previously, a peer-based ability to "poll" is a valuable feature to some applications. Fluence nodes come with a set of built-in services including the ability to store scripts on a peer with the intent of periodic execution.
@@ -120,7 +119,6 @@ In order to upload the periodic "block to db poll", we can use parts of the _eth
 )
 ```
 
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 ```bash
 # script file to string variable
 AIR=`cat air-scripts/ethqlite_block_committer.clj`
@@ -184,10 +182,9 @@ Particle id: 5fb0af87-310f-4b12-8c73-e044cfd8ef6e. Waiting for results... Press
 And we are golden. Give it some time and start checking Ethqlite for latest block and reward info!!
 
 {% hint style="info" %}
-Unfortunately, our daemonized service won't work just yet as the current implementation cannot take the \(client\) seed we need in order to get our SQLite write working. It's on the to-do list but if you need it, please contact us and we'll see about juggling priorities. 
+Unfortunately, our daemonized service won't work just yet as the current implementation cannot take the \(client\) seed we need in order to get our SQLite write working. It's on the to-do list but if you need it, please contact us and we'll see about juggling priorities.
 {% endhint %}
 
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 For completeness sake, let's remove the stored service with the following AIR script:
 
 ```bash
@@ -199,7 +196,6 @@ For completeness sake, let's remove the stored service with the following AIR sc
 
 As Aquamarine advances a particle's journey through the network, output from a service at workflow sequence s-1 method tends to be the input for a service at sequence s method. For example, the _hex\_to\_int_ method, as used earlier, takes the output from the _get\_latest\_block_ method. With single parameter outputs, this is a pretty straight forward and inherently decoupled dependency relation. However, when result parameters become more complex, such as structs, we still would like to keep services as decoupled as possible.
 
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 Fluence provides this capability by facilitating the conversion of \(Rust\) struct returns into [json values](https://github.com/fluencelabs/aquamarine/blob/master/interpreter-lib/src/execution/boxed_value/jvaluable.rs#L30). This allows json type key-value access to a desired subset of return values. If you got back to the _ethqlite.clj_ script, you may notice some fancy `$`, `!` operators tucked away in the deep recesses of parenthesis stacking. Below the pertinent snippet:
 
 ```text
@@ -247,7 +243,6 @@ pub struct RewardBlock {
 
 and the input expectations of _get\_miner\_rewards_, also an ethqlite service method, with the following [function](https://github.com/fluencelabs/examples/blob/c508d096e712b7b22aa94641cd6bb7c2fdb67200/multi-service/ethqlite/src/crud.rs#L177) signature: `pub fn get_miner_rewards(miner_address: String) -> MinerRewards`.
 
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 Basically, _get\_miner\_rewards_ wants an Ethereum address as a `String` and in the context of our AIR script we want to get the value from the _get\_reward\_block_ result. Rather than tightly coupling _get\_miner\_rewards_ to _get\_reward\_block_ in terms of, say, the _RewardBlock_ input parameter, we take advantage of the Fluence capability to turn structs into json strings and then supply the relevant key to extract the desired value. Specifically, we use the `$` operator to access the json representation at the desired index and the `!` operator to flatten the value, if desired.
 
 For example,

development_development/development_reward_block_app/development_eth_calls.md

@@ -104,7 +104,6 @@ modules_dir = "artifacts/"
     name = "block_getter"
 ```
 
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 If you haven't done so already, run `./scripts/build.sh` to compile the projects. Once we have _wasm_ files and the service configuration, we can check out our accomplishments with the REPL:
 
 ```bash
@@ -342,7 +341,5 @@ Particle id: 930ea13f-1474-4501-862a-ca5fad22ee42. Waiting for results... Press
 ===================
 ```
 
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 Right on! Our two services coordinate into the intended application returning the reward data for the latest block. Before we move on, locate the corresponding services on the Fluence testnet via the [ dashboard](https://dash.fluence.dev/), update your command-line with the appropriate service and node ids and run the same AIR script. Congratulations, you just run an app coordinated by distributed services!
 
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development_development/development_reward_block_app/development_persisting_blocks.md

@@ -106,7 +106,6 @@ The script extends our previous incarnation by adding only one more method: `upd
      "api_key": "MC5H2NK6ZIPMR32U7D4W35AWNNVCQX1ENH"}'
 ```
 
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 and run the AIR script with the revised `fldist` command:
 
 ```bash

development_development/development_reward_block_app/development_sqlite.md

@@ -83,7 +83,6 @@ wget https://github.com/fluencelabs/sqlite/releases/download/v0.10.0_w/sqlite3.w
 mv sqlite3.wasm artifacts/
 ```
 
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 Run `./build.sh` and check the artifacts for the expected wasm files
 
 Like all Fluence services, Ethqlite needs a [service configuration](https://github.com/fluencelabs/examples/blob/main/multi-service/ethqlite/Config.toml) file, which looks a little more involved than what we have seen so far.
@@ -114,7 +113,6 @@ name = "ethqlite"
 
 Let's break it down:
 
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 * the first \[\[module\]\] section 
   * specifies the _sqlite3.wasm_ module we pulled from the repo, 
   * allocates memory, where each page is about 64KB, and 
@@ -371,7 +369,6 @@ Particle id: 2fb4a366-6f40-46c1-9329-d77c6d03dfad. Waiting for results... Press
 ===================
 ```
 
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 If you run the init script again, you will receive an error _"Service already initiated"_, so we can be reasonably confident our code is working and it looks like our Ethqlite service is up and running on the local node.
 
 Due to the security concerns for our database, it is not advisable, or even possible, to use an already deployed Sqlite service from the Fluence Dashboard. Instead, we deploy our own instance with our own \(secret\) client seed. To determine which network nodes are available, run:

development_development/developmet_build_modules.md

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 In Fluence, a service is based on one or more [Wasm](https://webassembly.org/) modules suitable to be deployed to the Fluence Compute Engine \(FCE\). In order to develop our modules, we use Rust and the [Fluence Rust SDK](https://github.com/fluencelabs/rust-sdk).
 
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 ## Preliminaries
 
 The general process to create a Fluence \(module\) project is to:
@@ -13,7 +12,6 @@ cargo +nightly create your_module_name --release
 
 and add the [binary target](https://doc.rust-lang.org/cargo/reference/cargo-targets.html#binaries) and [Flunece Rust SDK](https://crates.io/crates/fce) to the Cargo.toml:
 
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 ```text
 <snip>
 
@@ -111,7 +109,6 @@ modules_dir = "artifacts/"
 
 The source code for the module can be found in the [examples repo](https://github.com/fluencelabs/examples/tree/main/greeting).
 
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 ## Taking The Greeting Module For A Spin
 
 Now that we have a Wasm module and service configuration, we can explore and test our achievements locally with the Fluence REPL tool `fce-repl`. Load the service for inspection and testing:
@@ -260,7 +257,6 @@ client seed: AgZjbuMvZmCWbqZBABXXtv3cjGTqYFfiVj7aqg8dm2fA
 client peerId: 12D3KooWFhUMisVC2VtXAertXt5oQQ7Xj1qppFZRM4mvEQ1iUaBP
 relay peerId: 12D3KooWQQYXh78acqBNuL5p1J5tmH4XCKLCHM21tMb8pcxqGL17
 [{"config":{"logger_enabled":true,"logging_mask":null,"mem_pages_count":100,"mounted_binaries":null,"wasi":{"envs":null,"mapped_dirs":null,"preopened_files":[]}},"hash":"c8aec6cbbc0a9632bf532b9553092ae6f66d2e3a5f71e11d1fe65e423c2204e2","name":"greeting"},{"config":{"logger_enabled":true,"logging_mask":null,"mem_pages_count":100,"mounted_binaries":null,"wasi":{"envs":null,"mapped_dirs":null,"preopened_files":[]}},"hash":"915d7487d4ae99f6136a7fe053c4ebd52cde1650c47492a315287117cedd0d3a","name":"greeting"}]
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 ```
 
 Which confirms our recent upload!!
@@ -279,7 +275,6 @@ Now that we have a service on our local node, we need to construct our AIR scrip
 
 As we've seen in the Quick Start section, we call the service _"greeting"_ with service id _service_ and the method parameter _name_. As usual, we use the `fldist` tool to execute the AIR script:
 
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 ```bash
 fldist --node-id 12D3KooWQQYXh78acqBNuL5p1J5tmH4XCKLCHM21tMb8pcxqGL17  --node-addr /ip4/127.0.0.1/tcp/9999/ws/p2p/12D3KooWQQYXh78acqBNuL5p1J5tmH4XCKLCHM21tMb8pcxqGL17 run_air -p greeting.clj -d '{"service":"9712f9ca-7dfd-4ff5-817d-aef9e1e92e03", "name": "Fluence"}'
 ```

development_development/summary.md

@@ -8,5 +8,3 @@ The further your understanding, please checkout the remainder of the documentati
 * The [Tutorials](../tutorials_tutorials/) section offers a a set of how-to's ranging from node and network configuration to fairly large-scale application development.
 * The [Recipes](../recipes_recipes/) section provides a \(growing\) set of pattern and pattern-like best practices and approaches.
 
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