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TALA is currently closed-source. For more information on licensing, please visit https://terrastruct.com/tala.

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# TALA
TALA is a diagram layout engine designed specifically for software architecture diagrams,
though it works well in other domains too. TALA is closed-source (for now). This
repository is primarily for installation instructions. You can also use this repository to
report issues, ask questions, and request features.
To learn more about TALA, please visit
[https://terrastruct.com/tala](https://terrastruct.com/tala).
To compare TALA with other layout engines, please visit
[https://text-to-diagram.com](https://text-to-diagram.com/?a=d2&layout_a=tala&b=d2&layout_b=dagre&example=chess).
## Rendering samples
You can see the `.d2` text for these in [./docs/d2](./docs/d2). Samples are
[generated](./ci/generate_samples.sh) through the CLI.
|<img src="./docs/assets/sample_1.svg" />|<img src="./docs/assets/sample_2.svg" />|<img src="./docs/assets/sample_3.svg" />|
|:-------------------------:|:-------------------------:|:-------------------------:|
|<img src="./docs/assets/sample_4.svg" />|<img src="./docs/assets/sample_5.svg" />|<img src="./docs/assets/sample_6.svg" />|
## Installation
```sh
curl -fsSL https://d2lang.com/install.sh | sh -s -- --tala --dryrun
# If things look good, install for real.
curl -fsSL https://d2lang.com/install.sh | sh -s -- --tala
```
You can also find binaries
[Releases](https://github.com/terarstruct/TALA/releases) page for Linux and MacOS, for
both AMD and ARM. Download the appropriate one for your OS to a directory
in your path. Windows coming soon.
### Post-install
Check that it was installed properly:
```sh
d2 layout tala
```
It should print out information about TALA. If not, please see troubleshooting steps.
### Use TALA
You are now ready to use TALA to layout your diagrams! Specify that D2 should use TALA by
setting the environment variable `D2_LAYOUT`.
```sh
D2_LAYOUT=tala d2 in.d2 out.svg
```
### Add API token (optional)
You may skip this step if you are just evaluating.
If you have an API token, copy and paste it into your environment variables. E.g.
```bash
export TSTRUCT_TOKEN = "tstruct_..."
```
## License keys
TALA is freely installable and locally runnable for evaluation. You'll need an API token
from your Terrastruct account to run it out of evaluation mode. Alternatively, if you
don't want to create an account, you can purchase a personal license key for TALA that
allows you to run TALA forever with access to updates for 12 months
[here](https://buy.stripe.com/bIYeXL3cT2Lr23e5ko). If you'd like to get the benefits of a
paid Terrastruct account at any time after, your license key will allow that.
## Troubleshooting
### TALA not found
```sh
which d2plugin-tala
```
If this does not return a directory, then `d2plugin-tala` is not in your path.
```sh
echo $PATH
```
Please move the downloaded binary to one of those locations.
### License warning prints even with API token
In the same terminal that you're running `d2` on, run
```sh
echo $TSTRUCT_TOKEN
```
If it is not found, then your environment variable has not been set in your shell. Make
sure you add it to the appropriate shell config file and `source` it in the running
terminal.
### Other
Please open an Issue describing what you're running into and we're happy to help. If you'd
like to remain anonymous/private, feel free to email us at info@terrastruct.com
## FAQ
### Does TALA use the internet?
The TALA plugin do not collect telemetry or use the internet in any way except to ping to
check the status of a license. This is only done when necessary, e.g. if you purchased a
month subscription, TALA will ping at the start of the next month and renew automatically
if the subscription is ongoing. If you purchased a year, it won't ping for a year. The
only data that's sent in these pings is the API token itself. No diagrams or anything
else leaves your computer.
### Others
Full FAQ at [https://terrastruct.com/tala](https://terrastruct.com/tala).

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#!/bin/sh
set -eu
cd -- "$(dirname "$0")/.."
THEME_INDEX=0
# alternate between cool and warm
themes=(0 100 1 102 4 104 5 105 6)
for f in ./docs/d2/*.d2
do
echo "Processing $f"
filename=$(basename -- "$f")
filename="${filename%.*}"
D2_LAYOUT=tala d2 --theme=${themes[$THEME_INDEX]} --debug $f ./docs/assets/${filename}.svg
let THEME_INDEX=${THEME_INDEX}+1
done

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runner: JobRunner {
shape: class
-start: datetime
-end: datetime
-running_server: string
-threads: int
-manager: JobsManager
+pipeline: Pipeline
+setPipeline(Pipeline p): void
+kickoff(threads int): bool
}
jobsUI: JobRunner UI {
kickoff
halt
}
batch: Batch {
manager: BatchManager {
shape: class
-num: int
-timeout: int
-pid
+getStatus(): Enum
+getJobs(): "Job[]"
+setTimeout(seconds int)
}
systemd: Systemd
selenium: Selenium
systemd -> manager: Ensure alive
manager -> selenium: Run job
}
jobsUI -> runner: Kick off
runner -> batch.manager: Queue jobs

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logs: {
shape: page
style.multiple: true
}
user: AT&T Customer {shape: person}
network: Network {
tower: Cell Tower {
satellites: {
shape: stored_data
style.multiple: true
}
transmitter
satellites -> transmitter
satellites -> transmitter
satellites -> transmitter
}
processor: Data Processor {
storage: Storage {
shape: cylinder
style.multiple: true
}
}
portal: Online Portal {
UI
}
tower.transmitter -> processor: phone logs
}
server: API Server
user -> network.tower: Make call
network.processor -> server
network.processor -> server
network.processor -> server
server -> logs
server -> logs
server -> logs: persist
server -> network.portal.UI: display
user -> network.portal.UI: access {
style.stroke-dash: 3
}

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SSR: Server side render {
templates: User defined templates
tests: Validation tests
engine: Rendering Engine\n API {
ingestion: Ingestion module {
shape: hexagon
}
fetch: Data fetching module {
shape: hexagon
}
schema: Schema version module {
shape: hexagon
}
}
next: NextJS
db: Data
templates -> engine.ingestion
engine.fetch <-> db: Integrate user data
engine.schema <-> db: Get version
engine <-> tests
}
build: Final build {
html: Rendered HTML
}
SSR.engine -> SSR.next -> build.html

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payment
AWS {
orchestrator: Orchestrator
airflow: Apache Airflow {
queue1: Queue 1 {
shape: queue
}
queue2: Queue 2 {
shape: queue
}
queue3: Queue 3 {
shape: queue
}
queue4: Queue 4 {
shape: queue
}
}
orchestrator -> airflow.queue1
orchestrator -> airflow.queue2
orchestrator -> airflow.queue3
orchestrator -> airflow.queue4
}
payment -> AWS.orchestrator
backup: Data backup
AWS.airflow.queue3 -> backup
AWS.airflow.queue4 -> backup
data: Data warehouse
AWS.airflow.queue1 -> data
AWS.airflow.queue2 -> data
local: On-prem backups {
queue1: Queue 1 {
shape: queue
}
queue2: Queue 2 {
shape: queue
}
}
backup -> local.queue1
backup -> local.queue2

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tenant: Offsite Tenant {
style.shadow: true
accountant: Accountant
security: Security
janitor: Janitor
}
tenant.accountant -> Quickbooks
auditors: Auditors
auditors -> Quickbooks
auditors -> IRS
Quickbooks -> Clients
dataroom: E335 Data Room {
style.shadow: true
style.fill: "#e9edef"
style.stroke-width: 2
whitelist: Whitelisted Server {
shape: rectangle
style.stroke-dash: 10
style.stroke: "#000E3D"
routing: Routing blocker
headers: Add header tokens
}
platform: Microsoft Windows Fleet {
style.stroke-dash: 4
style.stroke: "#000E3D"
build
}
whitelist <-> platform: Maintain connection {style.stroke-width: 4}
}
auditors -> dataroom.platform.build
auditors -> dataroom.whitelist.routing
tenant.accountants -> dataroom
logging: Web based logger
production: Data for prod {
style.shadow: true
network: Network availability {
style.stroke-dash: 10
style.stroke: "#000E3D"
instances: App instances {
style.multiple: true
}
db: App DBs {
shape: cylinder
}
secrets: AWS Secrets
containers: Docker containers
instances -> db
instances -> secrets
containers -> db
containers -> secrets
}
}
dataroom.whitelist.routing -> production.network.instances
dataroom.whitelist.headers -> production.network.instances
production.network.instances -> backups

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ibm: IBM "Espresso" CPU {
core0: IBM PowerPC "Broadway" Core 0
core1: IBM PowerPC "Broadway" Core 1
core2: IBM PowerPC "Broadway" Core 2
rom: 16 KB ROM
core0 -- core2
rom -> core2
}
amd: AMD "Latte" GPU {
mem: Memory & I/O Bridge
dram: DRAM Controller
edram: 32 MB EDRAM "MEM1"
rom: 512 B SEEPROM
sata: SATA IF
exi: EXI (Efficient XML Interchange)
gx: GX {
3 MB 1T-SRAM
}
radeon: AMD Radeon R7xx "GX2"
mem -- gx
mem -- radeon
rom -- mem
mem -- sata
mem -- exi
dram -- sata
dram -- exi
}
ddr3: 2 GB DDR3 RAM "MEM2"
amd.mem -- ddr3
amd.dram -- ddr3
amd.edram -- ddr3
ibm.core1 -- amd.mem
amd.exi -- RTC