{"id":3202,"date":"2026-02-26T05:46:34","date_gmt":"2026-02-26T05:46:34","guid":{"rendered":"https:\/\/renewasoft.com.tr\/?p=3202"},"modified":"2026-03-01T15:27:59","modified_gmt":"2026-03-01T15:27:59","slug":"zaman-serisi-verisi-timescale-tsdb-yaklasimiyla-scada-verisi-nasil-saklanir-partition-retention-sorgu-performansi","status":"publish","type":"post","link":"https:\/\/renewasoft.com.tr\/index.php\/tr\/2026\/02\/26\/zaman-serisi-verisi-timescale-tsdb-yaklasimiyla-scada-verisi-nasil-saklanir-partition-retention-sorgu-performansi\/","title":{"rendered":"Zaman Serisi Verisi: Timescale\/TSDB Yakla\u015f\u0131m\u0131yla SCADA Verisi Nas\u0131l Saklan\u0131r? (Partition, Retention, Sorgu Performans\u0131)"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text css=””]Zaman Serisi Verisi: Timescale\/TSDB Yakla\u015f\u0131m\u0131yla SCADA Verisi Nas\u0131l Saklan\u0131r? (Partition, Retention, Sorgu Performans\u0131)<\/strong><\/p>\n

\"\"<\/p>\n

Enerji tesislerinde ger\u00e7ek zamanl\u0131 izleme b\u00fcy\u00fcd\u00fck\u00e7e, \u201cveriyi topluyor muyuz?\u201d sorusu h\u0131zla \u201cbu veriyi y\u0131llarca nas\u0131l saklar\u0131z ve h\u00e2l\u00e2 h\u0131zl\u0131 sorgular\u0131z?\u201d sorusuna d\u00f6n\u00fc\u015f\u00fcr. SCADA taraf\u0131nda saniyelik (hatta daha s\u0131k) telemetri; kW, debi, kapak, titre\u015fim, s\u0131cakl\u0131k gibi y\u00fczlerce\u2013binlerce tag \u00fczerinden s\u00fcrekli akar. Birka\u00e7 ay i\u00e7inde sat\u0131r say\u0131s\u0131 milyar seviyelerine yakla\u015fabilir. Bu noktada klasik ili\u015fkilisel tablo tasar\u0131m\u0131yla devam etmek; indeks \u015fi\u015fmesi, sorgu gecikmeleri, maliyet art\u0131\u015f\u0131 ve bak\u0131m zorlu\u011fu ile sonu\u00e7lan\u0131r.<\/p>\n

Bu yaz\u0131da, zaman serisi verisi (time-series) i\u00e7in TSDB yakla\u015f\u0131m\u0131n\u0131 ve Postgres ekosisteminde \u00f6ne \u00e7\u0131kan Timescale benzeri \u201chypertable + chunk\u201d modelini SCADA ba\u011flam\u0131nda anlataca\u011f\u0131z. Ama\u00e7; hem ger\u00e7ek zamanl\u0131 panolar\u0131 milisaniye\u2013saniye d\u00fczeyinde h\u0131zl\u0131 tutmak, hem de y\u0131ll\u0131k ar\u015fivi y\u00f6netilebilir maliyetle saklamak. Bunun i\u00e7in partition\/chunk stratejisi, retention politikalar\u0131, downsampling (\u00f6zet veri), s\u0131k sorgu tipleri ve performans\/operasyon pratiklerini bir b\u00fct\u00fcn olarak ele alaca\u011f\u0131z.<\/p>\n

1) TL;DR<\/strong><\/p>\n

    \n
  1. SCADA verisi \u201cy\u00fcksek hacimli yazma + zaman aral\u0131\u011f\u0131 sorgular\u0131\u201d problemidir; zaman bazl\u0131 b\u00f6l\u00fcmlendirme (partition\/chunk) \u015fartt\u0131r [1][2].<\/li>\n
  2. \u201cHer \u015feyi ham veri olarak sonsuza kadar tutmak\u201d s\u00fcrd\u00fcr\u00fclemez; retention + downsampling ile \u201cham veri k\u0131sa, \u00f6zet veri uzun\u201d piramidi kurulmal\u0131d\u0131r [3][4].<\/li>\n
  3. Do\u011fru indeksler ve do\u011fru chunk\/partition boyu; hem yazma h\u0131z\u0131n\u0131 hem de sorgu gecikmesini belirler. PostgreSQL\u2019in declarative partitioning yakla\u015f\u0131m\u0131 bu temel mant\u0131\u011f\u0131 sa\u011flar [2].<\/li>\n
  4. Sorgular\u0131n b\u00fcy\u00fck b\u00f6l\u00fcm\u00fc birka\u00e7 paterne indirgenir: son X dakika trendi, belirli zaman aral\u0131\u011f\u0131nda agregasyon, alarm sonras\u0131 inceleme, dashboard KPI. TSDB tasar\u0131m\u0131 bu paternlere g\u00f6re yap\u0131lmal\u0131d\u0131r.<\/li>\n
  5. Hydrowise taraf\u0131nda hedef; SCADA entegrasyonundan gelen zaman serisini \u201chot\u2013warm\u2013cold\u201d katmanlar\u0131yla saklamak, KPI\u2019lar\u0131 s\u00fcrekli \u00fcretmek ve alarm\/analitik ekranlar\u0131n\u0131 h\u0131zl\u0131 tutmakt\u0131r.<\/li>\n<\/ol>\n

    2) Kavramlar ve teorik arka plan<\/strong><\/p>\n

    2.1 Zaman serisi (time-series) verisi nedir?<\/strong><\/p>\n

    Zaman serisi verisi; her kayd\u0131n bir zaman damgas\u0131 (timestamp) ta\u015f\u0131d\u0131\u011f\u0131, genellikle sens\u00f6r\/\u00f6l\u00e7\u00fcm ak\u0131\u015f\u0131 \u015feklinde olu\u015fan veridir. SCADA\u2019da bu, tag bazl\u0131 \u00f6l\u00e7\u00fcmler (kW, debi, titre\u015fim RMS vb.) olarak kar\u015f\u0131m\u0131za \u00e7\u0131kar. Temel \u00f6zellikler \u015funlard\u0131r:<\/p>\n

      \n
    • Yazma yo\u011funlu\u011fu y\u00fcksektir (append-only e\u011filim).<\/li>\n
    • Sorgular \u00e7o\u011funlukla zaman aral\u0131\u011f\u0131na dayan\u0131r (last 15 min, today, last 30 days).<\/li>\n
    • Agregasyon \u00e7ok s\u0131k kullan\u0131l\u0131r (ortalama, max, min, percentiles, rollup).<\/li>\n
    • \u201cCardinality\u201d (tag say\u0131s\u0131 ve etiket kombinasyonlar\u0131) b\u00fcy\u00fcd\u00fck\u00e7e maliyet artar.<\/li>\n<\/ul>\n

      2.2 TSDB yakla\u015f\u0131m\u0131 neden gerekir?<\/strong><\/p>\n

      Klasik ili\u015fkisel modelde tek b\u00fcy\u00fck tablo + indeksler, zamanla a\u011f\u0131rla\u015f\u0131r. Zaman serisi i\u00e7in pratik \u00e7\u00f6z\u00fcm; veriyi zaman eksenine g\u00f6re do\u011fal olarak b\u00f6lmek (partition) ve \u201ceski veriyi\u201d ar\u015fiv\/\u00f6zet seviyesine indirmektir. PostgreSQL, declarative partitioning ile tablolar\u0131 par\u00e7alara ay\u0131rmay\u0131 destekler [2]. Timescale benzeri TSDB yakla\u015f\u0131mlar\u0131nda ise bu par\u00e7alar genellikle \u201cchunk\u201d olarak otomatik y\u00f6netilir; zaman aral\u0131\u011f\u0131na g\u00f6re yeni chunk\u2019lar a\u00e7\u0131l\u0131r ve sorgu, ilgili chunk\u2019lara y\u00f6nlendirilir [1].<\/p>\n

      Teknik Not: Hypertable + Chunk Mant\u0131\u011f\u0131<\/strong><\/p>\n

        \n
      • Hypertable: Mant\u0131ksal olarak tek tablo gibi g\u00f6r\u00fcnen, fiziksel olarak zaman aral\u0131klar\u0131na b\u00f6l\u00fcnm\u00fc\u015f yap\u0131.<\/li>\n
      • Chunk interval: Her par\u00e7an\u0131n kapsad\u0131\u011f\u0131 zaman dilimi (\u00f6r. 1 g\u00fcn, 7 g\u00fcn).<\/li>\n
      • Ama\u00e7: Yazmay\u0131 paralelize etmek, eski veriye eri\u015fimi filtrelemek ve bak\u0131m i\u015flemlerini (compression\/retention) chunk baz\u0131nda \u00e7al\u0131\u015ft\u0131rmak [1].<\/li>\n<\/ul>\n

        (Kaynak: [1])<\/p>\n

        3) Nas\u0131l \u00e7al\u0131\u015f\u0131r? TSDB tasar\u0131m\u0131n\u0131n yap\u0131 ta\u015flar\u0131<\/strong><\/p>\n

        3.1 Veri modelleme: \u201cnarrow\u201d m\u0131 \u201cwide\u201d m\u0131?<\/strong><\/p>\n

        SCADA\u2019da iki temel \u015fema yakla\u015f\u0131m\u0131 vard\u0131r:<\/p>\n

          \n
        1. A) Narrow (\u00f6l\u00e7\u00fcm tablosu): (time, tag_id, value, quality, status)<\/li>\n
        2. B) Wide (sat\u0131rda \u00e7ok kolon): (time, kW, debi, kapak, titre\u015fim, \u2026)<\/li>\n<\/ol>\n

          Pratikte SCADA tag say\u0131s\u0131 b\u00fcy\u00fcd\u00fck\u00e7e \u201cwide\u201d yap\u0131 y\u00f6netilemez h\u00e2le gelir. Narrow yap\u0131; yeni tag eklemeyi kolayla\u015ft\u0131r\u0131r ve zaman serisi i\u015f y\u00fck\u00fcne daha uygundur. Bunun bedeli; do\u011fru indeks ve do\u011fru partition\/chunk ayar\u0131 gerektirmesidir. Timescale ekosisteminde \u201ctek hypertable m\u0131, \u00e7oklu tablo mu?\u201d gibi modelleme kararlar\u0131 da bu nedenle \u00f6nemlidir [5].<\/p>\n

          3.2 Partition\/Chunk stratejisi<\/strong><\/p>\n

          Zaman bazl\u0131 b\u00f6l\u00fcmlendirme (partitioning), sorgu performans\u0131n\u0131n temelidir. PostgreSQL\u2019de partitioning, tabloyu partition key\u2019e g\u00f6re par\u00e7alara b\u00f6ler ve \u201cpartition pruning\u201d ile gereksiz par\u00e7alar sorgu d\u0131\u015f\u0131 b\u0131rak\u0131labilir [2]. Timescale dok\u00fcmantasyonunda chunk interval mant\u0131\u011f\u0131; zaman serisinin otomatik chunk\u2019lara ayr\u0131lmas\u0131 \u015feklinde a\u00e7\u0131klan\u0131r [1].<\/p>\n

          Chunk\/partition boyunu nas\u0131l se\u00e7eriz?<\/p>\n

            \n
          • \u00c7ok k\u00fc\u00e7\u00fck par\u00e7a: \u00c7ok fazla chunk \u2192 metadata\/bak\u0131m maliyeti.<\/li>\n
          • \u00c7ok b\u00fcy\u00fck par\u00e7a: Sorgu taramas\u0131 artar \u2192 gecikme y\u00fckselir.<\/li>\n<\/ul>\n

            SCADA\u2019da tipik pratik: ham veride 1 g\u00fcn\u20137 g\u00fcn chunk, \u00f6zet veride daha b\u00fcy\u00fck aral\u0131klar.<\/p>\n

            3.3 Retention: \u201cne kadar saklayaca\u011f\u0131z?\u201d<\/strong><\/p>\n

            SCADA verisini sonsuza kadar ham tutmak maliyetlidir. TSDB yakla\u015f\u0131m\u0131 genellikle \u201chot\u2013warm\u2013cold\u201d piramidi uygular:<\/p>\n

              \n
            • Hot: saniyelik ham veri (7\u201330 g\u00fcn)<\/li>\n
            • Warm: 1 dk \/ 5 dk \u00f6zet (3\u201312 ay)<\/li>\n
            • Cold: 1 saat \/ 1 g\u00fcn \u00f6zet (1\u20135 y\u0131l veya ar\u015fiv)<\/li>\n<\/ul>\n

              InfluxDB dok\u00fcmantasyonu, retention policy ve continuous queries ile downsampling + expiry (eskitme) modelini net \u015fekilde anlat\u0131r [3][4]. Bu fikir Timescale taraf\u0131nda da continuous aggregate + retention\/compression politikalar\u0131yla benzer \u015fekilde uygulan\u0131r; hypertable\/continuous aggregate \u00fczerinde politika tan\u0131mlanabilir [6].<\/p>\n

              \"\"<\/strong><\/p>\n

              3.4 Downsampling: \u201cham veriyi \u00f6zetle\u201d<\/strong><\/p>\n

              Downsampling, eski ham veriyi daha d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckte \u00f6zet veriye d\u00f6n\u00fc\u015ft\u00fcrmektir. \u00d6rne\u011fin:<\/p>\n

                \n
              • 1 saniyelik ham \u2192 1 dakikal\u0131k ortalama\/max\/min<\/li>\n
              • 1 dakikal\u0131k \u2192 1 saatlik ortalama + percentiles<\/li>\n<\/ul>\n

                Bu sayede:<\/p>\n

                  \n
                • Dashboard sorgular\u0131 h\u0131zlan\u0131r (daha az sat\u0131r).<\/li>\n
                • Depolama maliyeti d\u00fc\u015fer.<\/li>\n
                • Uzun d\u00f6nem trendler okunabilir olur.<\/li>\n<\/ul>\n

                  3.5 Compression: \u201ceski chunk\u2019lar\u0131 s\u0131k\u0131\u015ft\u0131r\u201d<\/strong><\/p>\n

                  Zaman serisinde veri \u00e7o\u011funlukla append-only oldu\u011fu i\u00e7in \u201ceski veri\u201d \u00e7ok de\u011fi\u015fmez. Bu, s\u0131k\u0131\u015ft\u0131rma i\u00e7in idealdir. Timescale ekosisteminde hypertable\u2019larda compression ve compression policy gibi mekanizmalarla eski chunk\u2019lar otomatik s\u0131k\u0131\u015ft\u0131r\u0131labilir [6].<\/p>\n

                  Risk Kutusu: TSDB Tasar\u0131m\u0131nda En S\u0131k Hatalar<\/strong><\/p>\n

                    \n
                  • Ham veriyi s\u0131n\u0131rs\u0131z tutmak (maliyet patlar).<\/li>\n
                  • Cardinality\u2019yi kontrol etmemek (tag\/label patlamas\u0131).<\/li>\n
                  • Chunk interval\u2019i rastgele se\u00e7mek (ya \u00e7ok k\u00fc\u00e7\u00fck ya \u00e7ok b\u00fcy\u00fck).<\/li>\n
                  • \u0130ndeksleri \u201cher \u015feye\u201d basmak (yazma performans\u0131 d\u00fc\u015fer).<\/li>\n
                  • Zaman senkronizasyonu (NTP\/PTP) olmadan veri toplamak (tarih kaymalar\u0131).<\/li>\n<\/ul>\n

                    (Kaynak dayana\u011f\u0131: partition\/retention prensipleri [2][3][4])<\/p>\n

                    4) SCADA sorgu tipleri: Tasar\u0131m\u0131 belirleyen 4 ana kullan\u0131m<\/strong><\/p>\n

                    4.1 \u201cSon X dakika\u201d ger\u00e7ek zamanl\u0131 trend<\/strong><\/p>\n

                    Operat\u00f6r ekranlar\u0131 ve alarm panolar\u0131 genelde son 5\u201330 dakikay\u0131 \u00e7eker. Bu sorgular i\u00e7in:<\/p>\n

                      \n
                    • \u0130ndeks: (tag_id, time DESC) veya time odakl\u0131 indeks<\/li>\n
                    • Chunk: k\u00fc\u00e7\u00fck ama a\u015f\u0131r\u0131 k\u00fc\u00e7\u00fck olmayan aral\u0131k (1 g\u00fcn iyi ba\u015flang\u0131\u00e7)<\/li>\n
                    • Cache: dashboard katman\u0131nda k\u0131sa s\u00fcreli cache<\/li>\n<\/ul>\n

                      4.2 \u201cBelirli zaman aral\u0131\u011f\u0131nda agregasyon\u201d<\/strong><\/p>\n

                      Enerji tesislerinde KPI (ortalama g\u00fc\u00e7, g\u00fcnl\u00fck debi, max titre\u015fim) i\u00e7in tipik sorgu paterni:<\/p>\n

                        \n
                      • WHERE time BETWEEN \u2026<\/li>\n
                      • GROUP BY time_bucket(\u2026) \/ date_trunc(\u2026)<\/li>\n<\/ul>\n

                        Bu noktada downsampling\/continuous aggregate b\u00fcy\u00fck fark yarat\u0131r.<\/p>\n

                        4.3 \u201cAlarm sonras\u0131 adli inceleme\u201d<\/strong><\/p>\n

                        Alarmdan \u00f6nce\/sonra 10\u201330 dakikal\u0131k pencerede \u00e7oklu tag korelasyonu istenir. Bu sorgular:<\/p>\n

                          \n
                        • \u00c7ok tag \u2192 y\u00fcksek cardinality<\/li>\n
                        • Zaman penceresi dar \u2192 do\u011fru indeks \u00e7ok kritik<\/li>\n<\/ul>\n

                          Bu kullan\u0131m i\u00e7in \u201ctag gruplar\u0131\u201d (\u00fcnite bazl\u0131) ve sorgu optimizasyonu \u00f6nemlidir.<\/p>\n

                          4.4 \u201cUzun d\u00f6nem raporlama ve kar\u015f\u0131la\u015ft\u0131rma\u201d<\/strong><\/p>\n

                          Ayl\u0131k\u2013y\u0131ll\u0131k raporlarda ham veri yerine \u00f6zet tablolardan okumak gerekir. Aksi takdirde milyarlarca sat\u0131r taran\u0131r. Retention + downsampling piramidi bu y\u00fczden zorunludur [3][4].<\/p>\n

                          5) \u00d6rnek senaryo: HES\u2019te TSDB tasar\u0131m\u0131n\u0131 ad\u0131m ad\u0131m kurmak<\/strong><\/p>\n

                          Varsay\u0131m:<\/p>\n

                            \n
                          • 1.500 tag<\/li>\n
                          • Ortalama 1 saniyede 1 \u00f6l\u00e7\u00fcm\/tag (1 Hz)<\/li>\n
                          • G\u00fcnl\u00fck kay\u0131t \u2248 1.500 \u00d7 86.400 = 129.600.000 sat\u0131r\/g\u00fcn<\/li>\n<\/ul>\n

                            \"\"<\/p>\n

                            Bu hacimde tek tablo yakla\u015f\u0131m\u0131 h\u0131zla zorlan\u0131r. TSDB yakla\u015f\u0131m\u0131yla \u015fu plan uygulanabilir:<\/p>\n

                            Ad\u0131m 1 \u2014 \u00d6l\u00e7\u00fcm \u015femas\u0131 (narrow)<\/p>\n

                            measurements(time, tag_id, value, quality)<\/p>\n

                            \u0130ndeks: (tag_id, time) ve time bazl\u0131 partition\/chunk [2].<\/p>\n

                            Ad\u0131m 2 \u2014 Chunk interval se\u00e7imi<\/p>\n

                            Ham veride 1 g\u00fcn chunk (operasyonel sorgular son 30 g\u00fcn). Mant\u0131k: g\u00fcn bazl\u0131 bak\u0131m ve pruning kolayl\u0131\u011f\u0131.<\/p>\n

                            Ad\u0131m 3 \u2014 Retention plan\u0131<\/p>\n

                              \n
                            • Hot (ham, 1 saniye): 30 g\u00fcn<\/li>\n
                            • Warm (1 dk \u00f6zet): 12 ay<\/li>\n
                            • Cold (1 saat \u00f6zet): 5 y\u0131l<\/li>\n<\/ul>\n

                              Ad\u0131m 4 \u2014 Downsampling kural\u0131<\/p>\n

                              30 g\u00fcnden eski ham veriyi silmeden \u00f6nce, 1 dakikal\u0131k ve 1 saatlik \u00f6zetleri \u00fcret.<\/p>\n

                              InfluxDB\u2019nin RP + CQ yakla\u015f\u0131m\u0131ndaki gibi \u201c\u00f6nce \u00f6zetle, sonra expire\u201d mant\u0131\u011f\u0131 izlenir [3][4].<\/p>\n

                              Ad\u0131m 5 \u2014 Compression<\/p>\n

                              30 g\u00fcnden eski chunk\u2019lar s\u0131k\u0131\u015ft\u0131r\u0131l\u0131r; sorgu nadir ama gerekti\u011finde eri\u015filebilir olur [6].<\/p>\n

                              \u015eekil 1 \u2014 Hot\u2013Warm\u2013Cold retention piramidi (ham veri k\u0131sa, \u00f6zet veri uzun).<\/p>\n

                              A\u00e7\u0131klama: Depolama maliyeti kontrol edilirken raporlama performans\u0131 korunur.<\/p>\n

                              \u015eekil 2 \u2014 SCADA \u00f6l\u00e7\u00fcm hatt\u0131: Edge buffer \u2192 TSDB ingest \u2192 Continuous aggregates \u2192 Dashboard\/Alarm ekran\u0131.<\/p>\n

                              A\u00e7\u0131klama: Kesinti senaryosunda veri kayb\u0131n\u0131 azaltan mimari ak\u0131\u015f\u0131 g\u00f6sterir.<\/p>\n

                               <\/p>\n

                              6) Hydrowise \/ Renewasoft yakla\u015f\u0131m\u0131: \u201cSCADA verisi sadece depolanmaz, i\u015fletilir\u201d<\/strong><\/p>\n

                              Hydrowise taraf\u0131nda zaman serisi yakla\u015f\u0131m\u0131n\u0131 \u00fc\u00e7 katmanda d\u00fc\u015f\u00fcnebilirsiniz:<\/p>\n

                              \"\"<\/strong><\/p>\n

                              1) Veri katman\u0131 (ingest + kalite)<\/strong><\/p>\n

                                \n
                              • OPC UA\/SCADA entegrasyonu ile tag ak\u0131\u015f\u0131<\/li>\n
                              • Zaman damgas\u0131 standartla\u015ft\u0131rma (NTP\/PTP)<\/li>\n
                              • Kalite bayraklar\u0131 (quality) ile \u201cg\u00fcvenilir \u00f6l\u00e7\u00fcm\u201d ayr\u0131m\u0131<\/li>\n<\/ul>\n

                                 <\/p>\n

                                2) TSDB katman\u0131 (performans + maliyet)<\/strong><\/p>\n

                                  \n
                                • Hypertable\/chunk veya PostgreSQL partitioning mant\u0131\u011f\u0131yla zaman bazl\u0131 b\u00f6l\u00fcmlendirme [1][2]<\/li>\n
                                • Retention + downsampling plan\u0131 (hot\u2013warm\u2013cold) [3][4]<\/li>\n
                                • S\u0131k\u0131\u015ft\u0131rma ve ar\u015fiv politikalar\u0131 [6]<\/li>\n<\/ul>\n

                                  3) \u00dcr\u00fcn katman\u0131 (operasyonel de\u011fer)<\/strong><\/p>\n

                                    \n
                                  • KPI dashboard (kW, debi, verim, titre\u015fim trendleri)<\/li>\n
                                  • Alarm ekranlar\u0131 ve olay inceleme<\/li>\n
                                  • Bak\u0131m\/ar\u0131za analiti\u011fi i\u00e7in \u00f6zellik \u00e7\u0131kar\u0131m\u0131 (feature store mant\u0131\u011f\u0131)<\/li>\n<\/ul>\n

                                    \u0130\u00e7 link \u00f6nerileri (site i\u00e7i):<\/p>\n

                                      \n
                                    • \/hydrowise\/scada-entegrasyonu<\/li>\n
                                    • \/hydrowise\/gercek-zamanli-izleme<\/li>\n
                                    • \/hydrowise\/predictive-maintenance<\/li>\n
                                    • \/renewasoft\/ot-guvenligi<\/li>\n<\/ul>\n

                                      D\u0131\u015f link (otorite kaynak):<\/p>\n

                                        \n
                                      • PostgreSQL Table Partitioning (resmi dok\u00fcmantasyon) [2]<\/li>\n
                                      • InfluxDB Downsampling & Retention Guide (resmi dok\u00fcmantasyon) [3]<\/li>\n<\/ul>\n

                                        7) Bilgi Kart\u0131: \u201cTSDB tasar\u0131m kontrol listesi\u201d<\/strong><\/p>\n

                                          \n
                                        • Zaman senkronizasyonu var m\u0131 (NTP\/PTP)?<\/li>\n
                                        • Tag envanteri ve cardinality kontrol ediliyor mu?<\/li>\n
                                        • Chunk\/partition interval se\u00e7imi test edildi mi?<\/li>\n
                                        • Retention piramidi tan\u0131ml\u0131 m\u0131 (hot\u2013warm\u2013cold)?<\/li>\n
                                        • Downsampling job\/continuous aggregate var m\u0131?<\/li>\n
                                        • \u0130ndeksler sorgu paternlerine g\u00f6re mi?<\/li>\n
                                        • Backfill ve kesinti senaryosu planland\u0131 m\u0131?<\/li>\n
                                        • \u0130zleme: yazma h\u0131z\u0131, disk, sorgu gecikmesi, bloat \u00f6l\u00e7\u00fcl\u00fcyor mu?<\/li>\n<\/ul>\n

                                          (Kaynak: partitioning ve retention prensipleri [2][3][4])<\/p>\n

                                          8) S\u0131k sorulan sorular (FAQ)<\/strong><\/p>\n

                                          1) Timescale\/TSDB kullanmak zorunlu mu?<\/strong><\/p>\n

                                          Zorunlu de\u011fil; ancak saniyelik SCADA ak\u0131\u015f\u0131nda zaman bazl\u0131 partitioning ve retention plan\u0131 olmadan s\u00fcrd\u00fcr\u00fclebilir performans sa\u011flamak zordur [2][3].<\/p>\n

                                          2) Chunk interval\u2019i ka\u00e7 olmal\u0131?<\/strong><\/p>\n

                                          Tek do\u011fru yok. Ham veri i\u00e7in 1 g\u00fcn\u20137 g\u00fcn aral\u0131\u011f\u0131 yayg\u0131nd\u0131r. En do\u011fru se\u00e7im; yazma h\u0131z\u0131, sorgu paternleri ve bak\u0131m maliyeti ile test edilerek belirlenir [1][2].<\/p>\n

                                          3) Ham veriyi ka\u00e7 g\u00fcn tutmal\u0131y\u0131m?<\/strong><\/p>\n

                                          Operasyonel ihtiya\u00e7 belirler. Saha pratiklerinde 7\u201330 g\u00fcn ham, daha uzun d\u00f6nem i\u00e7in \u00f6zet veri yakla\u015f\u0131m\u0131 yayg\u0131nd\u0131r [3][4].<\/p>\n

                                          4) Downsampling neyi bozabilir?<\/strong><\/p>\n

                                          Yanl\u0131\u015f agregasyon se\u00e7ilirse k\u0131sa s\u00fcreli pikler kaybolabilir. Bu y\u00fczden max\/min ve percentiles gibi \u00f6zetleri de saklamak gerekir.<\/p>\n

                                          5) \u201cBad actor\u201d (g\u00fcr\u00fclt\u00fcl\u00fc tag) TSDB\u2019yi etkiler mi?<\/strong><\/p>\n

                                          Evet. \u00c7ok s\u0131k de\u011fi\u015fen veya hatal\u0131 sens\u00f6rler yazma hacmini art\u0131r\u0131r; hem depolamay\u0131 hem de sorguyu etkiler. Bu tag\u2019ler i\u00e7in ayr\u0131 izleme ve kalite kontrolleri \u00f6nerilir.<\/p>\n

                                          6) SCADA verisini buluta ta\u015f\u0131rken en kritik risk nedir?<\/strong><\/p>\n

                                          Zaman senkronizasyonu, veri b\u00fct\u00fcnl\u00fc\u011f\u00fc ve OT g\u00fcvenli\u011fi (segmentasyon, yetkilendirme, loglama) birlikte ele al\u0131nmal\u0131d\u0131r [7].<\/p>\n

                                          7) PostgreSQL partitioning yeterli mi, \u00f6zel TSDB \u015fart m\u0131?<\/strong><\/p>\n

                                          PostgreSQL partitioning g\u00fc\u00e7l\u00fc bir temeldir [2]. TSDB \u00e7\u00f6z\u00fcmleri ise otomasyon (chunk y\u00f6netimi, s\u0131k\u0131\u015ft\u0131rma, continuous aggregates) sa\u011flayarak operasyon y\u00fck\u00fcn\u00fc azaltabilir [1][6].<\/p>\n

                                          9) Sonu\u00e7<\/strong><\/p>\n

                                          SCADA zaman serisi verisi, b\u00fcy\u00fcd\u00fck\u00e7e klasik tablo yakla\u015f\u0131m\u0131n\u0131 zorlayan bir i\u015f y\u00fck\u00fcne d\u00f6n\u00fc\u015f\u00fcr. \u00c7\u00f6z\u00fcm; zaman bazl\u0131 b\u00f6l\u00fcmlendirme (partition\/chunk), retention plan\u0131 ve downsampling piramidini birlikte tasarlamakt\u0131r. Bu \u00fc\u00e7l\u00fc; hem ger\u00e7ek zamanl\u0131 panolar\u0131 h\u0131zl\u0131 tutar, hem de y\u0131ll\u0131k ar\u015fiv maliyetini kontrol eder [2][3][4].<\/p>\n

                                          Uygulanabilir bir sonraki ad\u0131m:<\/p>\n

                                          1) Tag envanterinizi \u00e7\u0131kar\u0131n ve g\u00fcnl\u00fck sat\u0131r hacmini hesaplay\u0131n.<\/strong><\/p>\n

                                          2) Hot\u2013warm\u2013cold retention hedeflerini belirleyin.<\/strong><\/p>\n

                                          3) Ham veride chunk\/partition interval\u2019i se\u00e7ip bir pilot tabloda test edin.<\/strong><\/p>\n

                                          4) 1 dk ve 1 saat \u00f6zet tablolar\u0131n\u0131 (downsampling) devreye al\u0131n.<\/strong><\/p>\n

                                          5) Hydrowise ile KPI paneli ve alarm ekranlar\u0131n\u0131z\u0131n sorgu paternlerine g\u00f6re indeks ve \u00f6zet stratejisini netle\u015ftirin.<\/strong><\/p>\n

                                          Kaynak\u00e7a<\/strong><\/p>\n

                                          [1] TigerData (Timescale). Hypertables documentation. 2025. (https:\/\/www.tigerdata.com\/docs\/use-timescale\/latest\/hypertables) Eri\u015fim: 2026-02-22<\/p>\n

                                          [2] PostgreSQL Global Development Group. Table Partitioning (DDL Partitioning). 2025. (https:\/\/www.postgresql.org\/docs\/current\/ddl-partitioning.html) Eri\u015fim: 2026-02-22<\/p>\n

                                          [3] InfluxData. Downsampling and Data Retention (InfluxDB documentation). 2016. (https:\/\/archive.docs.influxdata.com\/influxdb\/v1.0\/guides\/downsampling_and_retention\/) Eri\u015fim: 2026-02-22<\/p>\n

                                          [4] InfluxData. Downsampling and Data Retention (InfluxDB documentation). 2015. (https:\/\/archive.docs.influxdata.com\/influxdb\/v0.11\/guides\/downsampling_and_retention\/) Eri\u015fim: 2026-02-22<\/p>\n

                                          [5] TigerData (Timescale). Best Practices for Time-Series Data Modeling (hypertables vs multiple tables). 2024. (https:\/\/www.tigerdata.com\/learn\/best-practices-time-series-data-modeling-single-or-multiple-partitioned-tables-aka-hypertables) Eri\u015fim: 2026-02-22<\/p>\n

                                          [6] TigerData (Timescale). add_compression_policy() documentation. 2025. (https:\/\/www.tigerdata.com\/docs\/api\/latest\/compression\/add_compression_policy) Eri\u015fim: 2026-02-22<\/p>\n

                                          [7] Stouffer, K. et al. NIST SP 800-82 Rev. 3 \u2014 Guide to Operational Technology (OT) Security. 2023. (https:\/\/csrc.nist.gov\/pubs\/sp\/800\/82\/r3\/final) Eri\u015fim: 2026-02-22[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

                                          [vc_row][vc_column][vc_column_text css=””]Zaman Serisi Verisi: Timescale\/TSDB Yakla\u015f\u0131m\u0131yla SCADA Verisi Nas\u0131l Saklan\u0131r? (Partition, Retention, Sorgu Performans\u0131) Enerji tesislerinde ger\u00e7ek zamanl\u0131 izleme b\u00fcy\u00fcd\u00fck\u00e7e, \u201cveriyi topluyor muyuz?\u201d sorusu h\u0131zla \u201cbu veriyi y\u0131llarca nas\u0131l saklar\u0131z ve h\u00e2l\u00e2 h\u0131zl\u0131 sorgular\u0131z?\u201d sorusuna d\u00f6n\u00fc\u015f\u00fcr. SCADA taraf\u0131nda saniyelik (hatta daha s\u0131k) telemetri; kW, debi, kapak, titre\u015fim, s\u0131cakl\u0131k gibi y\u00fczlerce\u2013binlerce tag \u00fczerinden s\u00fcrekli akar. Birka\u00e7 […]<\/p>\n","protected":false},"author":5,"featured_media":3340,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[58,1861,60,1857,1873],"tags":[],"class_list":["post-3202","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-akilli-sebekeler-enerji-yonetimi","category-ariza-yonetimi-is-degeri","category-endustri-4-enerji-otomasyonu","category-enerji-piyasasi-epias-karar-destek","category-scada-iot-ve-veri-mimari"],"yoast_head":"\nZaman Serisi Verisi: Timescale\/TSDB Yakla\u015f\u0131m\u0131yla SCADA Verisi Nas\u0131l Saklan\u0131r? 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