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)
	0x001d 00029 (x.go:3)	FUNCDATA	$1, gclocals·33cdeccccebe80329f1fdbee7f5874cb(SB)

# 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:

        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))));

{
	"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" },

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
```

          "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"
    },

      }
    }

    // Testing multibyte escape sequences
    assertEscaping(e, "%00", '\u0000'); // nul
    assertEscaping(e, "%7F", '\u007f'); // del
    assertEscaping(e, "%C2%80", '\u0080'); // xx-00010,x-000000
    assertEscaping(e, "%DF%BF", '\u07ff'); // xx-11111,x-111111
    assertEscaping(e, "%E0%A0%80", '\u0800'); // xxx-0000,x-100000,x-00,0000
    assertEscaping(e, "%EF%BF%BF", '\uffff'); // xxx-1111,x-111111,x-11,1111