oraclecloud-performance-tuning

Oracle Cloud Performance Tuning

Overview

Navigate OCI's opaque shape naming, block volume performance tiers, and shape-dependent network bandwidth. OCI shapes like VM.Standard.E5.Flex, VM.Standard3.Flex, and VM.Standard.A1.Flex look similar but have wildly different performance profiles. Block volume tiers (Balanced, Higher Performance, Ultra High Performance) have different IOPS and throughput limits that are easy to get wrong. This skill maps performance characteristics to shapes and storage tiers so you can make informed infrastructure decisions.

Purpose: Choose the right compute shape and storage tier for your workload by understanding OCI's performance characteristics, and monitor those resources programmatically.

Prerequisites

• OCI tenancy with an API signing key in ~/.oci/config
• Python 3.8+ with pip install oci
• Compartment OCID for querying available shapes and metrics
• Basic understanding of IOPS, throughput, and OCPU concepts

Instructions

Step 1: Understand Shape Naming

OCI shape names encode processor generation, type, and flexibility:

Shape	Processor	OCPUs	Network Gbps per OCPU	Best For
VM.Standard.E5.Flex	AMD EPYC 9J14 (Genoa)	1–94	1 Gbps	General workloads (latest gen)
VM.Standard.E4.Flex	AMD EPYC 7J13 (Milan)	1–64	1 Gbps	General workloads
VM.Standard3.Flex	Intel Xeon (Ice Lake)	1–32	1 Gbps	Intel-optimized software
VM.Standard.A1.Flex	Ampere Altra (ARM)	1–80	1 Gbps	ARM-native, cost-efficient
VM.Optimized3.Flex	Intel Xeon (Ice Lake)	1–18	4 Gbps	HPC, network-intensive
BM.Standard.E5.192	AMD EPYC 9J14	192	100 Gbps total	Bare metal, full isolation

Key insight: Flex shapes let you choose OCPU and memory independently. Memory defaults to 1 GB/OCPU min, 64 GB/OCPU max (varies by shape). Network bandwidth scales linearly with OCPUs up to the shape maximum.

Step 2: Query Available Shapes

Discover what shapes are available in your tenancy and region:

import oci

config = oci.config.from_file("~/.oci/config")
compute = oci.core.ComputeClient(config)

shapes = compute.list_shapes(
    compartment_id="ocid1.compartment.oc1..example"
).data

for shape in shapes:
    print(
        f"{shape.shape}: "
        f"OCPUs={shape.ocpus or 'flex'}, "
        f"Memory={shape.memory_in_gbs or 'flex'} GB, "
        f"Network={shape.networking_bandwidth_in_gbps} Gbps"
    )

Step 3: Block Volume Performance Tiers

OCI block volumes have three performance tiers. IOPS and throughput scale with volume size:

Tier	IOPS / GB	Max IOPS	Throughput / GB	Max Throughput	Cost Multiplier
Balanced	60	25,000	480 KB/s	480 MB/s	1x (default)
Higher Performance	75	35,000	600 KB/s	480 MB/s	~1.7x
Ultra High Performance	90–225	300,000	720 KB/s–2.4 MB/s	2.4 GB/s	~3.3x+

Example: A 1 TB Balanced volume gets 25,000 IOPS and 480 MB/s throughput. The same 1 TB on Ultra High Performance gets up to 225,000 IOPS and 2.4 GB/s.

Step 4: Create a Performance-Tuned Block Volume

config = oci.config.from_file("~/.oci/config")
block_storage = oci.core.BlockstorageClient(config)

# Create a Higher Performance tier volume
volume = block_storage.create_volume(
    oci.core.models.CreateVolumeDetails(
        compartment_id="ocid1.compartment.oc1..example",
        availability_domain="Uocm:US-ASHBURN-AD-1",
        display_name="high-perf-data-vol",
        size_in_gbs=500,
        vpus_per_gb=20  # 10=Balanced, 20=Higher, 30-120=Ultra High
    )
).data

print(f"Volume created: {volume.id}")
print(f"Performance: {volume.vpus_per_gb} VPUs/GB")

The vpus_per_gb parameter controls the tier:

• 10 = Balanced (default)
• 20 = Higher Performance
• 30–120 = Ultra High Performance (scales IOPS linearly)

Step 5: Monitor Performance Metrics

Query actual performance data from running instances and volumes:

from datetime import datetime, timedelta

monitoring = oci.monitoring.MonitoringClient(config)

# Query disk IOPS for a specific instance
response = monitoring.summarize_metrics_data(
    compartment_id="ocid1.compartment.oc1..example",
    summarize_metrics_data_details=oci.monitoring.models.SummarizeMetricsDataDetails(
        namespace="oci_computeagent",
        query='DiskIopsRead[5m].mean() + DiskIopsWritten[5m].mean()',
        start_time=(datetime.utcnow() - timedelta(hours=1)).isoformat() + "Z",
        end_time=datetime.utcnow().isoformat() + "Z"
    )
)

for metric in response.data:
    for dp in metric.aggregated_datapoints:
        print(f"{dp.timestamp}: {dp.value:.0f} total IOPS")

Step 6: Network Bandwidth Validation

Verify you're getting expected network performance:

# Query network bytes for bandwidth validation
response = monitoring.summarize_metrics_data(
    compartment_id="ocid1.compartment.oc1..example",
    summarize_metrics_data_details=oci.monitoring.models.SummarizeMetricsDataDetails(
        namespace="oci_computeagent",
        query='NetworksBytesIn[5m].rate() + NetworksBytesOut[5m].rate()',
        start_time=(datetime.utcnow() - timedelta(hours=1)).isoformat() + "Z",
        end_time=datetime.utcnow().isoformat() + "Z"
    )
)

for metric in response.data:
    for dp in metric.aggregated_datapoints:
        gbps = (dp.value * 8) / 1_000_000_000
        print(f"{dp.timestamp}: {gbps:.2f} Gbps")

Output

Successful completion produces:

• A shape comparison showing processor, OCPU range, and network bandwidth
• Available shapes queried from your tenancy and region
• A block volume created with the appropriate performance tier
• Performance monitoring queries showing actual IOPS and throughput
• Network bandwidth validation against expected shape limits

Error Handling

Error	Code	Cause	Solution
NotAuthorizedOrNotFound	404	Shape not available in your region/AD	Check availability with list_shapes; try a different AD
LimitExceeded	400	Tenancy service limit reached	Request limit increase in Console > Governance > Limits
InvalidParameter	400	Invalid vpus_per_gb value	Use 10, 20, or 30–120 (multiples of 10)
TooManyRequests	429	Rate limited on metric queries	Reduce query frequency; widen time intervals
InternalError	500	OCI service issue	Check OCI Status
NotAuthenticated	401	Bad config or expired key	Verify ~/.oci/config and regenerate API key if needed

Examples

Quick shape lookup with OCI CLI:

# List all flex shapes available in your compartment
oci compute shape list \
  --compartment-id ocid1.compartment.oc1..example \
  --query "data[?contains(shape, 'Flex')].{Shape:shape, OCPUs:ocpus, Memory:\"memory-in-gbs\"}" \
  --output table

# Check block volume performance tier
oci bv volume get \
  --volume-id ocid1.volume.oc1..example \
  --query "data.{Name:\"display-name\", SizeGB:\"size-in-gbs\", VPUsPerGB:\"vpus-per-gb\"}"

Right-size an instance based on CPU metrics:

import oci
from datetime import datetime, timedelta

config = oci.config.from_file("~/.oci/config")
monitoring = oci.monitoring.MonitoringClient(config)

# Get 7-day average CPU to check if over-provisioned
response = monitoring.summarize_metrics_data(
    compartment_id="ocid1.compartment.oc1..example",
    summarize_metrics_data_details=oci.monitoring.models.SummarizeMetricsDataDetails(
        namespace="oci_computeagent",
        query='CpuUtilization[1h].mean()',
        start_time=(datetime.utcnow() - timedelta(days=7)).isoformat() + "Z",
        end_time=datetime.utcnow().isoformat() + "Z"
    )
)

for metric in response.data:
    avg_cpu = sum(dp.value for dp in metric.aggregated_datapoints) / len(metric.aggregated_datapoints)
    if avg_cpu < 20:
        print(f"Instance {metric.dimensions.get('resourceId', 'unknown')}: "
              f"avg CPU {avg_cpu:.1f}% — consider downsizing")

Resources

• OCI Compute Shapes — full shape specifications
• OCI Block Volume Performance — IOPS/throughput by tier
• OCI Python SDK — SDK reference
• OCI Pricing — cost per OCPU, storage, and network
• OCI API Reference — REST API specs

Next Steps

After optimizing shapes and storage, proceed to oraclecloud-cost-tuning to track spend and set budget alerts, or see oraclecloud-observability to set up ongoing performance monitoring with alarms.