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Simpan dan kategorikan konten berdasarkan preferensi Anda.
Halaman ini menjelaskan cara mengoptimalkan performa cluster AlloyDB for PostgreSQL Anda menggunakan
rekomendasi cluster yang kurang memadai.
Pemberi rekomendasi membantu Anda mendeteksi cluster yang memiliki pemakaian CPU dan memori yang tinggi serta memberikan rekomendasi untuk meningkatkan konfigurasi cluster Anda.
Cara kerja pemberi rekomendasi cluster yang tidak disediakan
Jika terdeteksi pemakaian CPU dan/atau memori yang tinggi, Anda akan melihat
rekomendasi untuk meningkatkan ukuran instance yang terpengaruh di cluster
guna mengurangi pemakaian CPU atau memori pada puncaknya. Rekomendasi dibuat setiap hari.
Sebelum memulai
Sebelum Anda dapat melihat rekomendasi dan insight, lakukan tindakan berikut:
Menampilkan daftar rekomendasi cluster yang tidak disediakan
Anda dapat membuat daftar rekomendasi untuk cluster yang tidak disediakan secara memadai
menggunakan konsol Google Cloud , gcloud CLI, atau Recommender API.
Konsol
Untuk menampilkan rekomendasi terkait cluster yang tidak disediakan, selesaikan langkah-langkah berikut:
Di kartu Performa, klik Instance utama yang kurang memadai.
Daftar cluster yang menerapkan rekomendasi Instance utama yang tidak disediakan akan ditampilkan.
gcloud CLI
Untuk membuat daftar rekomendasi terkait cluster yang tidak disediakan secara memadai menggunakan gcloud CLI, jalankan perintah gcloud recommender recommendations list sebagai berikut:
GET https://recommender.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/recommenders/google.alloydb.cluster.PerformanceRecommender/recommendations?filter=recommenderSubtype=INCREASE_PRIMARY_INSTANCE_SIZE
Ganti kode berikut:
PROJECT_ID: Project ID Anda.
LOCATION: Region tempat cluster Anda berada, seperti us-central1.
Lihat insight dan rekomendasi mendetail
Anda dapat melihat insight dan rekomendasi mendetail tentang cluster yang tidak disediakan secara memadai
yang memerlukan pengoptimalan menggunakan konsol Google Cloud ,
gcloud CLI, atau Recommender API.
GET https://recommender.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/insightTypes/google.alloydb.cluster.PerformanceInsight/insights?filter=insightSubtype=INSIGHT_SUBTYPE
Ganti kode berikut:
PROJECT_ID: Project ID Anda.
LOCATION: Region tempat cluster Anda berada, misalnya, us-central1.
INSIGHT_SUBTYPE: tetapkan parameter ini ke salah satu opsi berikut:
HIGH_INSTANCE_CPU_UTILIZATION: menampilkan
insight tentang penggunaan CPU
HIGH_INSTANCE_MEMORY_UTILIZATION: menampilkan
insight tentang memori
Tabel berikut berisi insight dan rekomendasi yang dapat dihasilkan oleh pemberi rekomendasi cluster yang tidak disediakan secara memadai oleh AlloyDB untuk PostgreSQL untuk membantu Anda menghindari bottleneck akibat penggunaan CPU dan memori yang tinggi serta meminimalkan kemungkinan peristiwa kehabisan memori.
Subjenis terlihat di hasil gcloud dan API.
Insight
Rekomendasi
Berdasarkan tren pemakaian CPU saat ini, cluster ditandai
memiliki penggunaan CPU yang tinggi.
Subjenis: HIGH_INSTANCE_CPU_UTILIZATION
Meningkatkan ukuran CPU atau mengurangi penggunaan CPU.
Subjenis: INCREASE_PRIMARY_INSTANCE_SIZE
Berdasarkan tren pemakaian memori saat ini, cluster ditandai
memiliki penggunaan memori yang tinggi.
Subjenis: HIGH_INSTANCE_MEMORY_UTILIZATION
Tambah ukuran memori atau kurangi penggunaan memori.
Subjenis: INCREASE_PRIMARY_INSTANCE_SIZE
Menerapkan rekomendasi menggunakan konsol Google Cloud
Evaluasi rekomendasi dengan cermat dan lakukan tindakan berikut di konsolGoogle Cloud untuk menerapkan rekomendasi:
Klik Edit di cluster Anda.
Di jendela Edit instance utama, beralihlah ke jenis mesin dengan lebih banyak vCPU dan memori yang lebih besar.
Anda tidak perlu menyesuaikan ukuran cluster persis seperti yang direkomendasikan. Gunakan penilaian
Anda dan ubah ukuran berdasarkan cara Anda ingin menyediakan cluster.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-04 UTC."],[[["\u003cp\u003eThe underprovisioned cluster recommender identifies clusters with high CPU and/or memory utilization and suggests optimizations to enhance performance.\u003c/p\u003e\n"],["\u003cp\u003eRecommendations to increase the instance size of underprovisioned clusters are generated daily and can be viewed after enabling the Recommender API and having the correct IAM roles.\u003c/p\u003e\n"],["\u003cp\u003eYou can list and apply underprovisioned cluster recommendations using the Google Cloud console, gcloud CLI, or the Recommender API.\u003c/p\u003e\n"],["\u003cp\u003eInsights on high CPU and memory utilization can be viewed via the console, CLI, or API, detailing the type of usage issue, such as \u003ccode\u003eHIGH_INSTANCE_CPU_UTILIZATION\u003c/code\u003e or \u003ccode\u003eHIGH_INSTANCE_MEMORY_UTILIZATION\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eImplementing the recommended instance size increase involves editing the cluster settings in the console, updating the primary instance to a machine type with more vCPUs and memory.\u003c/p\u003e\n"]]],[],null,["# Optimize underprovisioned clusters\n\nThis page describes how to optimize the performance of your AlloyDB for PostgreSQL clusters by using the\nunderprovisioned cluster [recommender](/recommender/docs/overview).\nThe recommender helps you detect clusters that have high CPU and memory\nutilization and provides recommendations for improving your cluster configuration.\n\nHow the underprovisioned cluster recommender works\n--------------------------------------------------\n\nWhen there is high CPU and or memory utilization detected, you see a\nrecommendation to increase the size of the affected instance in the cluster\nto reduce CPU or memory utilization at peak. Recommendations are generated daily.\n\nBefore you begin\n----------------\n\nBefore you can view recommendations and insights, do the following:\n\n- Ensure that you [enable the Recommender API](/recommender/docs/enabling).\n\n- To get the permissions to view and work with insights and recommendations,\n ensure that you have the required [Identity and Access Management (IAM) roles](/iam/docs/understanding-roles#cloud-alloydb-roles).\n\n \u003cbr /\u003e\n\n See [Grant access to other users](/alloydb/docs/user-grant-access) for more information.\n\nList underprovisioned cluster recommendations\n---------------------------------------------\n\nYou can list recommendations for underprovisioned clusters\nusing the Google Cloud console, `gcloud CLI`, or the Recommender API. \n\n### Console\n\nTo list recommendations about underprovisioned clusters, complete the following steps:\n\n1. In the Google Cloud console, go to the **Clusters** page.\n\n [Go to Clusters](https://console.cloud.google.com/alloydb/clusters)\n\n For more information, see\n [Find recommendations with Recommendation Hub](/recommender/docs/recommendation-hub/identify-configuration-problems).\n2. In the **Performance** card, click **Underprovisioned primary instance**.\n\n A list of clusters to which the **Underprovisioned primary instance** recommendation applies is displayed.\n\n### gcloud CLI\n\nTo list recommendations about underprovisioned clusters using gcloud CLI, run the [`gcloud recommender recommendations list`](/sdk/gcloud/reference/recommender/recommendations/list) command as follows: \n\n```\ngcloud recommender recommendations list \\\n--project=PROJECT_ID \\\n--location=LOCATION \\\n--recommender=google.alloydb.cluster.PerformanceRecommender \\\n--filter=recommenderSubtype=INCREASE_PRIMARY_INSTANCE_SIZE\n```\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e: A region where your clusters are located, such as `us-central1`.\n\n### API\n\nTo list recommendations for underprovisioned clusters using the [Recommendations API](/recommender/docs/using-api), call the\n[`recommendations.list`](/recommender/docs/reference/rest/v1/projects.locations.recommenders.recommendations/list)\nmethod as follows: \n\n```\nGET https://recommender.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/recommenders/google.alloydb.cluster.PerformanceRecommender/recommendations?filter=recommenderSubtype=INCREASE_PRIMARY_INSTANCE_SIZE\n```\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e: A region where your clusters are located, such as `us-central1`.\n\nView insights and detailed recommendations\n------------------------------------------\n\nYou can view insights and detailed recommendations about underprovisioned clusters\nthat require optimization using the Google Cloud console,\n`gcloud CLI`, or the Recommender API. \n\n### Console\n\n1. In the Google Cloud console, go to the **Clusters** page.\n\n [Go to Clusters](https://console.cloud.google.com/alloydb/clusters)\n2. Click the recommendation button for a cluster in the **Issues** column.\n\n The recommendation panel appears, which contains insights and detailed recommendations about an underprovisioned cluster.\n\n### gcloud CLI\n\nRun the [`gcloud recommender insights list`](/sdk/gcloud/reference/recommender/insights/list) command as follows: \n\n```\ngcloud recommender insights list \\\n--project=PROJECT_ID \\\n--location=LOCATION \\\n--insight-type=google.alloydb.cluster.PerformanceInsight\n--filter=insightSubtype=INSIGHT_SUBTYPE\n```\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e : A region where your clusters are located, such as `us-central1`.\n- \u003cvar translate=\"no\"\u003eINSIGHT_SUBTYPE\u003c/var\u003e: set this parameter to one of the following:\n - `HIGH_INSTANCE_CPU_UTILIZATION`: display insights about CPU usage\n - `HIGH_INSTANCE_MEMORY_UTILIZATION`: display insights about memory\n\n### API\n\nCall the [`insights.list`](/recommender/docs/reference/rest/v1/projects.locations.insightTypes.insights/list) method as follows: \n\n```\nGET https://recommender.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/insightTypes/google.alloydb.cluster.PerformanceInsight/insights?filter=insightSubtype=INSIGHT_SUBTYPE\n```\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e: A region where your clusters are located, for example, `us-central1`.\n- \u003cvar translate=\"no\"\u003eINSIGHT_SUBTYPE\u003c/var\u003e: set this parameter to one of the following:\n - `HIGH_INSTANCE_CPU_UTILIZATION`: display insights about CPU usage\n - `HIGH_INSTANCE_MEMORY_UTILIZATION`: display insights about memory\n\nThe following table lists the insights and recommendations that the AlloyDB for PostgreSQL\nunderprovisioned cluster recommender might generate to help you avoid bottlenecks from high CPU and memory\nusage and minimize the likelihood of out-of-memory events.\nThe subtypes are visible in the `gcloud` and API results.\n\nApply recommendations using the Google Cloud console\n----------------------------------------------------\n\nEvaluate the recommendations carefully and do the following in the\nGoogle Cloud console to implement the recommendation:\n\n1. Click **Edit** on your cluster.\n2. In the **Edit primary instance** window, switch to a machine type with more vCPUs and more memory.\n You don't need to rightsize the cluster exactly as recommended. Use your\n judgement and resize based on how you intend to provision the cluster.\n\n3. Click **Update instance**.\n\n | **Note:** You must carefully evaluate before you update the cluster. Applying recommendations might impact your pricing.\n\nWhat's next\n-----------\n\n- [Google Cloud recommenders](/recommender/docs/recommenders)"]]
