"".main STEXT size=74 args=0x0 locals=0x10
	0x0000 00000 (x.go:3)	TEXT	"".main(SB), $16-0
	0x0000 00000 (x.go:3)	MOVQ	(TLS), CX
	0x0009 00009 (x.go:3)	CMPQ	SP, 16(CX)
	0x000d 00013 (x.go:3)	JLS	67
	0x000f 00015 (x.go:3)	SUBQ	$16, SP
	0x0013 00019 (x.go:3)	MOVQ	BP, 8(SP)
	0x0018 00024 (x.go:3)	LEAQ	8(SP), BP
	0x001d 00029 (x.go:3)	FUNCDATA	$0, gclocals·33cdeccccebe80329f1fdbee7f5874cb(SB)

        assertEquals(0, new BigDecimal("0.001").compareTo(BigDecimalConversionUtil.toBigDecimal(new Double(0.001D))));
        assertEquals(0, new BigDecimal("0.0001").compareTo(BigDecimalConversionUtil.toBigDecimal(new Double(0.0001D))));
        assertEquals(0, new BigDecimal("0.00001").compareTo(BigDecimalConversionUtil.toBigDecimal(new Double(0.00001D))));

# ADR-0001 - Use Architectural Decision Records

## Date

2023-12-01

## Context

In a distributed team with many subteams, the best solution to communicate decisions is to use a format accessible by everyone in charge of development.

We use *Specification* and *Discovery* documents stored in Google Drive, but they present some downsides:

# ADR-0003 - Avoid introducing Groovy types to public API

## Date

2024-01-12

## Context

Gradle's public API requires equal access from all JVM-based languages.
Kotlin, Groovy, Java, and other JVM-based languages should be able to use the Gradle API without relying on another language's standard library.

Historically, Gradle has shipped with some Groovy types in very prominent APIs.

0.591 responseHeadersStart
0.675 responseHeadersEnd
0.676 responseBodyStart
0.679 responseBodyEnd
0.679 connectionReleased
0.680 callEnd
REQUEST 2 (pooled connection)
0.000 callStart
0.001 connectionAcquired
0.001 requestHeadersStart
0.001 requestHeadersEnd
0.002 responseHeadersStart
0.082 responseHeadersEnd
0.082 responseBodyStart
0.082 responseBodyEnd
0.083 connectionReleased
0.083 callEnd
```

{
	"id": "0001",
	"type": "donut",
	"name": "Cake",
	"ppu": 0.55,
	"batters":
		{
			"batter":
				[
					{ "id": "1001", "type": "Regular" },
					{ "id": "1002", "type": "Chocolate" },
					{ "id": "1003", "type": "Blueberry" },
					{ "id": "1004", "type": "Devil's Food" }
				]
		},
	"topping":
		[
			{ "id": "5001", "type": "None" },
			{ "id": "5002", "type": "Glazed" },
			{ "id": "5005", "type": "Sugar" },

          "datasource": {
            "type": "prometheus",
            "uid": "${datasource}"
          },
          "expr": "round(sum(irate(istio_requests_total{reporter=~\"$qrep\",destination_service=~\"$service\"}[5m])), 0.001)",
          "format": "time_series",
          "intervalFactor": 1,
          "refId": "A",
          "step": 4
        }
      ],
      "title": "Client Request Volume",
      "type": "stat"
    },

import org.junit.jupiter.api.Test;

import static org.junit.jupiter.api.Assertions.assertEquals;

class CLIReportingUtilsTest {

    @Test
    void testFormatDuration() {
        assertEquals("0.001 s", CLIReportingUtils.formatDuration(1));
        assertEquals("0.999 s", CLIReportingUtils.formatDuration(1000 - 1));
        assertEquals("1.000 s", CLIReportingUtils.formatDuration(1000));

Our aim is to keep the process lightweight and approachable.

            "uid": "${datasource}"
          },
          "expr": "round(sum(irate(istio_requests_total{reporter=~\"$qrep\",destination_workload_namespace=~\"$namespace\",destination_workload=~\"$workload\"}[5m])), 0.001)",
          "format": "time_series",
          "intervalFactor": 1,
          "refId": "A",
          "step": 4
        }
      ],
      "title": "Incoming Request Volume",
      "type": "stat"
    },