Sap Config Validator

When to Use

• "Run config checks for AB1" / "Validate configuration for AB3"
• "STAF checks for HSO" / "Run all configuration checks"
• "Check OS parameters" / "Check HANA configuration"

Topology handling (all 8 system types)

Derive HA_TYPE from architecture + deployment (see the values block below) and iterate nodes by topology:

• scale-out → validate every DB node (master + workers + standby) and compare sysctl / global.ini across all worker nodes — they must be uniform for HSR partitioning; flag any drift.
• standalone / distributed → HA_TYPE = "false"; skip the high_availability STAF checks.
• high-availability / disaster-recovery → HA_TYPE = scale_up|scale_out; run the HA checks. For disaster-recovery, also confirm the DR-region nodes have collected configs and validate them too.

Infrastructure Requirements

This skill requires a Storage Account in the ## Deployed Infrastructure section of Team Onboarding.

• If no Storage Account is listed — Respond exactly: "Config validation requires a Storage Account with collected SAP configs (the sap-configs container must exist and the agent MI must have Storage Blob Data Reader on it). No Storage Account is listed in Deployed Infrastructure. Run infra/deploy-sre-infra.ps1 to deploy one." Then stop. Do NOT attempt to fetch STAF or list blobs.
• If Storage Account is listed — Run the flow below. Configs are read from blob using the SRE Agent's own Managed Identity (built into RunAzCliReadCommands — --auth-mode login). No proxy is needed for this skill.

Architecture

   STAF check definitions                Collected configs
   (Azure/sap-automation-qa @ main)      (Azure Blob Storage)
            │                                    │
            │ requests.get                       │ az storage blob (--auth-mode login)
            │ (9 YAML files)                     │ uses SRE Agent UMI
            ▼                                    ▼
            └────────►  ExecutePythonCode  ◄─────┘
                              │
                              │ parse YAML → filter applicability →
                              │ extract actuals from collected files →
                              │ compare against expected_output/valid_list/min/max
                              ▼
                       compliance report

Rules

1. Never invent or fabricate checks. Every check ID, expected value, and reference must come from the STAF YAML fetched from GitHub. If GitHub is unreachable and no checks load, report the failure and stop.
2. Never deploy anything. No collector deployment, no VM commands, no infrastructure changes.
3. Never use az vm run-command. If configs are missing or stale, instruct the user to re-run the collector on the affected VM (via az vm run-command or their config-mgmt tool) so fresh configs land in the sap-configs blob.
4. Present results exactly as computed — do not add, remove, or modify any check result.

STAF YAML schema (verified against Azure/sap-automation-qa @ main)

Each YAML file has a top-level checks: list. Each check has:

• id, name, description, category, severity
• applicability — filter keys: os_type, os_version, role (singular: DB, SCS, ERS, APP, WEB, PAS), database_type, storage_type, workload, hardware_type, high_availability (bool, or list of scale_up/scale_out), high_availability_agent (ISCSI or AFA)
• collector_type — command (shell command run on the VM) or azure (ARM API lookup — not supported by this skill, returns not_evaluated)
• collector_args — for command: command: (shell string) and user: (root or sidadm). For azure: resource_type, property, optional mount_point
• validator_type — string, range, or list
• validator_args — expected_output (string), valid_list (list), min/max (range)
• report — check (compare actual vs expected — the only kind we evaluate), section (UI heading), or table (data-only)
• references — list of SAP Note / Microsoft docs URLs

Execution

Step 1 — Download collected configs from blob

Use RunAzCliReadCommands. The values for <storage>, <SID>, and <host> come from the Team Onboarding ## Deployed Infrastructure and ## SAP Landscape sections. Authentication is the agent's own Managed Identity (--auth-mode login) — no keys, no SAS.

# 1a. Confirm fresh configs exist
az storage blob list \
    --account-name <storage> --container-name sap-configs \
    --prefix "<SID>/<host>/latest/" --auth-mode login \
    --query "[].{name:name, modified:properties.lastModified}" -o json

# 1b. Download all files under latest/ to a local temp dir
az storage blob download-batch \
    --source sap-configs --pattern "<SID>/<host>/latest/*" \
    --destination /tmp/configs/<SID>/<host>/ \
    --account-name <storage> --auth-mode login

If the blob list is empty or the newest file is older than 14 days, stop and report: "No fresh collected configs found for <SID>/<host> in <storage>/sap-configs. The collector may not be installed on this VM. Re-run the collector on the VM (az vm run-command invoke -g <RG> -n <vm> --command-id RunShellScript --scripts 'sudo /opt/sre/collect-sap-configs.sh', or your config-management tool) so fresh configs land in the sap-configs blob, then re-run this validation."

Step 2 — Fetch STAF definitions and run the comparison

Use ExecutePythonCode. Set the six landscape variables at the top from the Team Onboarding inventory before running.

import json, re, requests, yaml
from pathlib import Path

# ── Values from Team Onboarding landscape inventory ─────────────────────────
SID          = "AB1"              # SAP System ID
HOST         = "AB1vm"            # VM hostname
OS_TYPE      = "SLES_SAP"         # SLES_SAP | REDHAT | OracleLinux | Windows
ROLES        = ["DB", "SCS", "PAS"]    # DB,SCS,ERS,APP,WEB,PAS  (ASCS auto-aliased to SCS)
DB_TYPE      = "HANA"             # HANA | Db2 | MSSQL | Oracle | ASE
STORAGE_TYPE = "Premium_LRS"      # Premium_LRS | UltraSSD_LRS | PremiumV2_LRS | AFS | ANF | StandardSSD_LRS | Standard_LRS
# HA_TYPE is the STAF applicability value — DERIVE it from the inventory's architecture + deployment:
#   deployment in (standalone, distributed)          -> "false"
#   deployment in (high-availability, disaster-recovery) and architecture == scale-up  -> "scale_up"
#   deployment in (high-availability, disaster-recovery) and architecture == scale-out -> "scale_out"
HA_TYPE      = "false"            # "false" (no HA) | "scale_up" | "scale_out"  (derived — see above)
HA_AGENT     = "none"             # none | AFA | ISCSI   (from ha.fencing: azure-fence-agent->AFA, sbd->ISCSI)

CONFIG_DIR = Path(f"/tmp/configs/{SID}/{HOST}/latest")  # populated by Step 1b

# ── 1. Fetch STAF check definitions from GitHub ─────────────────────────────
STAF_FILES = ["hana.yml", "sap.yml", "virtual_machine.yml", "network.yml",
              "ascs.yml", "app.yml", "high_availability.yml", "package.yml", "db2.yml"]
STAF_BASE  = ("https://raw.githubusercontent.com/Azure/sap-automation-qa"
              "/main/src/roles/configuration_checks/tasks/files")

def parse_yaml(text):
    """STAF YAML defines its anchor enums BELOW the checks that use them.
    PyYAML rejects forward references, so on failure we move `enums:` to the top and retry."""
    try:
        return yaml.safe_load(text)
    except yaml.YAMLError:
        m = re.search(r'^\s{0,2}enums:', text, re.MULTILINE)
        if not m:
            raise
        pos = text.rfind('\n', 0, m.start()) + 1
        return yaml.safe_load(text[pos:] + '\n' + text[:pos])

all_checks, fetch_errors = [], []
for fname in STAF_FILES:
    try:
        r = requests.get(f"{STAF_BASE}/{fname}", timeout=30)
        if r.status_code != 200:
            fetch_errors.append(f"{fname}: HTTP {r.status_code}"); continue
        parsed = parse_yaml(r.text)
        if parsed and "checks" in parsed:
            for chk in parsed["checks"]:
                chk["_source"] = fname
                all_checks.append(chk)
    except Exception as e:
        fetch_errors.append(f"{fname}: {e}")

if not all_checks:
    print(json.dumps({"error": "Failed to fetch STAF checks from GitHub",
                      "details": fetch_errors})); raise SystemExit

# ── 2. Filter by applicability (real STAF schema field names) ───────────────
roles = {("SCS" if r.upper() == "ASCS" else r.upper()) for r in ROLES}

def applies(chk):
    a = chk.get("applicability") or {}
    def in_list(value, target):
        lst = value if isinstance(value, list) else [value]
        return target in lst

    if "os_type"      in a and a["os_type"]      and not in_list(a["os_type"], OS_TYPE):           return False, "os_type"
    if "database_type" in a and a["database_type"] and not in_list(a["database_type"], DB_TYPE):    return False, "database_type"
    if "storage_type" in a and a["storage_type"] and not in_list(a["storage_type"], STORAGE_TYPE): return False, "storage_type"

    if "role" in a and a["role"]:
        rolelst = {x.upper() for x in (a["role"] if isinstance(a["role"], list) else [a["role"]])}
        if not (rolelst & roles):
            return False, "role"

    if "high_availability" in a and a["high_availability"] is not None:
        v = a["high_availability"]
        if HA_TYPE == "false":
            if v is True or (isinstance(v, list) and v):
                return False, "high_availability"
        else:
            if v is False:                              return False, "high_availability"
            if isinstance(v, list) and HA_TYPE not in v: return False, "high_availability"

    if "high_availability_agent" in a and a["high_availability_agent"] is not None:
        if HA_AGENT == "none":
            return False, "high_availability_agent"
        if not in_list(a["high_availability_agent"], HA_AGENT):
            return False, "high_availability_agent"

    return True, None

applicable, seen, filtered = [], set(), {}
for chk in all_checks:
    cid = chk.get("id", "")
    if cid in seen:
        continue
    ok, why = applies(chk)
    if ok:
        seen.add(cid); applicable.append(chk)
    elif why:
        filtered[why] = filtered.get(why, 0) + 1

evaluatable = [c for c in applicable if c.get("report") == "check" and c.get("validator_type")]
data_only   = [c for c in applicable if c not in evaluatable]

# ── 3. Load collected config files into memory ──────────────────────────────
configs = {}
if CONFIG_DIR.is_dir():
    for p in CONFIG_DIR.rglob("*"):
        if p.is_file():
            try:
                configs[str(p.relative_to(CONFIG_DIR)).replace("\\", "/")] = p.read_text(errors="replace")
            except Exception:
                pass

if not configs:
    print(json.dumps({"error": f"No collected configs found in {CONFIG_DIR}. Run Step 1 first."}))
    raise SystemExit

# ── 4. Extract actual values for the common command patterns ────────────────
# We only evaluate checks whose `collector_args.command` matches one of the
# patterns below — everything else is marked `not_evaluated` with a reason.
# This keeps the skill honest: we never compare against a value we couldn't
# actually find in the collected data.
def extract_actual(check):
    if check.get("collector_type") == "azure":
        return None, "azure_collector_not_supported_in_skill"

    cmd = (check.get("collector_args") or {}).get("command", "")
    if not cmd:
        return None, "no_command"

    # 1. sysctl: "/sbin/sysctl <param> -n"  → look up in os/sysctl-runtime.txt
    m = re.search(r'sysctl\s+([\w.]+)', cmd)
    if m:
        param = m.group(1)
        data = configs.get("os/sysctl-runtime.txt", "")
        if not data:
            return None, "missing:os/sysctl-runtime.txt"
        for line in data.split("\n"):
            if "=" in line:
                k, _, v = line.partition("=")
                if k.strip() == param:
                    return v.strip(), None
        return None, f"sysctl_param_not_found:{param}"

    # 2. df -T <mount>  → filesystem type column from os/disk-usage.txt
    m = re.search(r'df\s+-T\s+(\S+)', cmd)
    if m:
        mount = m.group(1)
        data = configs.get("os/disk-usage.txt", "")
        if not data:
            return None, "missing:os/disk-usage.txt"
        for line in data.strip().split("\n"):
            parts = line.split()
            # df -hT columns: Filesystem Type Size Used Avail Use% Mounted-on
            if len(parts) >= 7 and parts[-1] == mount:
                return parts[1], None
        return None, f"mount_not_found:{mount}"

    # 3. Transparent HugePages
    if "transparent_hugepage" in cmd:
        data = configs.get("os/thp-status.txt", "")
        if not data:
            return None, "missing:os/thp-status.txt"
        m2 = re.search(r'\[(\w+)\]', data)
        return (m2.group(1) if m2 else data.strip()), None

    # 4. tuned-adm active profile
    if "tuned-adm" in cmd:
        data = configs.get("os/tuned-profile.txt", "")
        return (data.strip(), None) if data else (None, "missing:os/tuned-profile.txt")

    # 5. fstrim systemctl active count
    if "fstrim" in cmd and "systemctl" in cmd:
        data = configs.get("os/fstrim-status.txt", "")
        if not data:
            return None, "missing:os/fstrim-status.txt"
        return str(sum(1 for l in data.split("\n") if "active" in l.lower())), None

    # 6. free -m Swap total
    if "free" in cmd and "Swap" in cmd:
        data = configs.get("os/free-m.txt", "")
        if not data:
            return None, "missing:os/free-m.txt"
        for line in data.split("\n"):
            if line.strip().startswith("Swap:"):
                parts = line.split()
                if len(parts) >= 2:
                    return parts[1], None
        return None, "swap_line_not_found"

    # 7. systemd DefaultTasksMax
    if "DefaultTasksMax" in cmd:
        data = configs.get("os/systemd-defaults.txt", "")
        if not data:
            return None, "missing:os/systemd-defaults.txt"
        for line in data.split("\n"):
            if "DefaultTasksMax" in line and "=" in line:
                return line.split("=", 1)[1].strip(), None
        return None, "DefaultTasksMax_not_found"

    # 8. softdog module loaded
    if "lsmod" in cmd and "softdog" in cmd:
        data = configs.get("os/lsmod-softdog.txt", "")
        if data is None:
            return None, "missing:os/lsmod-softdog.txt"
        return (data.strip() if data.strip() else "not loaded"), None

    # 9. uname -r
    if "uname" in cmd and "-r" in cmd:
        data = configs.get("os/uname-r.txt", "")
        return (data.strip(), None) if data else (None, "missing:os/uname-r.txt")

    return None, "unsupported_command_pattern"

# ── 5. Compare actual vs expected ───────────────────────────────────────────
def compare(check, actual):
    vtype = check.get("validator_type")
    vargs = check.get("validator_args") or {}
    if actual is None:
        return "not_evaluated", "actual value unavailable"
    actual_s = " ".join(str(actual).strip().split())

    if vtype == "string":
        expected = " ".join(str(vargs.get("expected_output", "")).strip().split())
        if actual_s.lower() == expected.lower():
            return "pass", f"actual={actual_s}"
        return "fail", f"actual={actual_s} | expected={expected}"

    if vtype == "range":
        try:
            n = float(actual_s)
        except ValueError:
            return "not_evaluated", f"actual={actual_s} not numeric"
        lo, hi = vargs.get("min"), vargs.get("max")
        if lo is not None and n < float(lo):
            return "fail", f"actual={n} below min={lo}"
        if hi is not None and n > float(hi):
            return "fail", f"actual={n} above max={hi}"
        return "pass", f"actual={n} in [{lo},{hi}]"

    if vtype == "list":
        valid = vargs.get("valid_list", []) or []
        if actual_s.lower() in [str(v).strip().lower() for v in valid]:
            return "pass", f"actual={actual_s}"
        return "fail", f"actual={actual_s} | valid_list={valid}"

    return "not_evaluated", f"unknown validator_type={vtype}"

# ── 6. Run every evaluatable check ──────────────────────────────────────────
results, failures = [], []
for chk in evaluatable:
    actual, skip = extract_actual(chk)
    if actual is None:
        status, detail = "not_evaluated", skip
    else:
        status, detail = compare(chk, actual)
    item = {
        "id":         chk.get("id"),
        "name":       chk.get("name"),
        "severity":   chk.get("severity"),
        "category":   chk.get("category"),
        "status":     status,
        "detail":     detail,
        "references": chk.get("references"),
    }
    results.append(item)
    if status == "fail":
        failures.append(item)

summary = {
    "pass":          sum(1 for r in results if r["status"] == "pass"),
    "fail":          len(failures),
    "not_evaluated": sum(1 for r in results if r["status"] == "not_evaluated"),
    "data_only":     len(data_only),
}

print(json.dumps({
    "sid": SID, "host": HOST,
    "data_sources": {
        "staf":    {"source": "github_live", "total": len(all_checks),
                    "fetch_errors": fetch_errors or None},
        "configs": {"source": "blob", "file_count": len(configs),
                    "dir": str(CONFIG_DIR)},
    },
    "staf": {
        "total":        len(all_checks),
        "filtered_out": filtered,
        "applicable":   len(applicable),
        "evaluatable":  len(evaluatable),
    },
    "summary": summary,
    "failures": failures,
    "results": results,
}, indent=2))

Step 3 — Format and present results

Use the printed JSON to produce a compliance report. Do not invent any check IDs, expected values, or references — quote them verbatim from the JSON.

Suggested format:

<SID> — STAF Config Compliance Report

  Data Sources:
    STAF checks:  GitHub live (<staf.total> total) — Azure/sap-automation-qa @ main
    Config data:  blob <configs.dir> (<configs.file_count> files)

  Check Coverage:
    Total STAF checks:    <staf.total>
    Filtered out:         <sum of filtered_out>  (os_type: N, role: N, ...)
    Applicable:           <staf.applicable>
    ├─ Evaluated:         <pass + fail>  (<pass> pass, <fail> fail)
    ├─ Not evaluated:     <not_evaluated>  (commands this skill does not extract — see below)
    └─ Data-only:         <data_only>  (info/section/table — no expected value)

  ══════════════════════════════════════
  FAILURES (list every entry from .failures verbatim):

  ❌ <id> <name> — <detail>   [<severity>]
     Refs: <references>
  ...

  ══════════════════════════════════════
  SUMMARY: <pass>/<evaluated> PASS, <fail> FAIL

Presentation rules:

• Show data_sources first so the user knows where the data came from.
• List every failure from report.failures — never truncate or summarize.
• If summary.not_evaluated is high, explain that this skill only extracts values for nine common command patterns (sysctl, df -T, THP, tuned-adm, fstrim, swap, DefaultTasksMax, softdog, uname). Cluster / ANF / Azure-collector / arbitrary-command checks return not_evaluated with a reason — that is expected, not a bug.
• If configs.file_count == 0, do not report any PASS/FAIL — say configs are missing and stop.
• If staf.fetch_errors is non-empty, surface it so the user knows partial STAF data was used.

References

• SAP Testing Automation Framework (STAF)
• STAF Check Definitions
• README adoption modes