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Dasar Rancangan Sistem


Motivasi

Belajar bagaimana merancang sistem skala besar.

Persiapan wawancara rancangan sistem.

Belajar bagaimana merancang sistem dalam skala besar

Belajar bagaimana merancang sistem terskalakan membantu menjadikan kita perekayasa yang lebih baik.

Perancangan sistem merupakan topik yang luas. Sumber daya mengenai prinsip-prinsip perancangan sistem banyak tersebar di berbagai situs di internet. Hal ini menyulitkan karena sumber daya tersebut tidak terkumpul di satu tempat.

Repositori ini menjadi tempat pengorganisasian koleksi-koleksi sumber daya yang dibutuhkan untuk belajar membangun sistem dalam sekala besar.

Belajar dari komunitas sumber terbuka

Proyek ini merupakan proyek sumber terbuka yang terus diperbarui.

Ayo kontribusi di sini!

Persiapan wawancara rancangan sistem

Selain wawancara pemrograman, perancangan sistem adalah salah satu komponen yang diwajibkan dari dari proses wawancara teknis di banyak perusahaan teknologi.

Latih pertanyaan-pertanyaan umum wawancara rancangan sistem dan bandingkan hasilmu dengan contoh solusi: diskusi, program, dan diagram.

Topik-topik tambahan untuk persiapan wawancara:

Kartu kilat Anki


Bungkusan kartu kilat Anki yang disediakan menggunakan perulangan berjeda untuk membantu menguasai konsep-konsep kunci rancangan sistem.

Cocok untuk digunakan ketika dalam perjalanan.

Sumber daya kode: Tantangan kode interaktif

Apakah Anda mencari sumber daya untuk membantu persiapan wawancara pemrograman?


Silakan periksa repositori Tantangan Pemrograman Interaktif yang berisi tambahan bungkusan Anki:

Kontribusi

Belajar dari komunitas.

Silakan kirim permintaan tarik (pull request) untuk membantu hal-hal berikut:

  • Membenarkan kesalahan
  • Memperbaiki bagian yang ada
  • Menambahkan bagian baru
  • Terjemahan

Konten yang masih memerlukan polesan ditempatkan di bagian dalam pengembangan

Tinjau kembali Pedomain Kontribusi.

Indeks topik rancangan sistem

Ringkasan dari berbagai topik rancangan sistem, termasuk kelebihan dan kekurangannya. Segala sesuatu dalam rancangan adalah hasil kompromi.

Setiap bagian berisi tautan ke sumber daya yang lebih dalam.


Panduan belajar

Topik yang disarankan untuk ditinjau ulang berdasarkan garis waktu wawancara (pendek, sedang, panjang).

Imgur

P: Apakah saya perlu mengetahui segala sesuatu yang ada di sini untuk wawancara?

J: Tidak. Anda tidak perlu tahu segala sesuatu yang ada di sini untuk persiapan wawancara.

Apa yang menjadi pertanyaan saat Anda wawancara bergantung pada hal-hal yang tidak tentu, contohnya:

  • Banyak pengalaman yang Anda miliki
  • Latar belakang teknis Anda
  • Posisi yang Anda lamar
  • Perusahaan tempat Anda wawancara
  • Keberuntungan

Kandidat yang berpengalaman umumnya diharapkan untuk tahu lebih mengenai rancangan sistem. Arsitek atau pemimpin tim mungkin diharapkan untuk tahu lebih banyak dibandingkan kontributor perorangan. Perusahan teknologi top kemungkinan besar mempunyai satu atau lebih sesi wawancara rancangan.

Mulai dari topik yang luas dan masuk lebih dalam ke beberapa area. Pengetahuan dasar berbagai topik-topik kunci rancangan sistem akan sangat membantu. Sesuaikan panduan berikut berdasarkan waktu, pengalaman, posisi yang dilamar, dan perusahan tempat Anda wawancara.

  • Garis waktu pendek - Bidik topik-topik rancangan sistem secara luas. Latih dengan cara menjawab beberapa pertanyaan wawancara.
  • Garis waktu sedang - Bidik topik-topik rancangan sistem secara luas dan perdalam dibeberapa bagian tertentu. Latih dengan cara menjawab banyak pertanyaan wawancara.
  • Garis waktu panjang - Bidik topik-topik rancangan sistem secara laus dan mendalam. Latih dengan cara menyelesaikan seluruh pertanyaan wawancara.
Short Medium Long
Baca sampai habis Indeks topik rancangan sistem untuk pemahaman secara luas bagaimana cara kerja suatu sistem 👍 👍 👍
Baca sampai habis beberapa artikel di Blog teknik perusahaan untuk perusahaan tempat Anda wawancara 👍 👍 👍
Baca sampai habis beberapa Arsitektur dunia nyata 👍 👍 👍
Ulas Pendekatan menjawab pertanyaan wawancara rancangan sistem 👍 👍 👍
Tinjau Pertanyaan wawancara rancangan sistem beserta solusinya Some Many Most
Teliti Pertanyaan wawancara rancangan berbasis objek beserta solusinya Some Many Most
Periksa Tambahan pertanyaan wawancara rancangan sistem Some Many Most

Pendekatan menjawab pertanyaan wawancara rancangan sistem

Cara menangani pertanyaan wawancara perancangan sistem.

Wawancara perancangan sistem adalah pembicaraan yang bersifat terbuka. Kita diharapkan untuk menuntun pembicaraan tersebut.

Kita dapat menggunakan langkah-langkah berikut untuk menuntun diskusi. Untuk memperkuat proses diskusi, ulas bagian Pertanyaan wawancara rancangan sistem beserta solusinya menggunakan langkah-langkah berikut.

Langkah 1: Uraikan kasus penggunaan, batasan, dan asumsi

Kumpulkan kebutuhan dan tentukan ruang lingkup permasalahan. Gunakan pertanyaan untuk memperjelas kasus penggunaan dan batasan. Diskusikan juga asumsi yang diambil.

  • Siapa pengguna sistem?
  • Bagaimana pengguna sistem akan menggunakan sistem tersebut?
  • Berapa banyak pengguna sistem?
  • Apa yang dilakukan oleh sistem?
  • Masukan dan keluaran apa yang ada pada sistem?
  • Berapa besar ekspektasi data yang perlu ditangani?
  • Berapa ekspektasi jumlah permintaan per detik?
  • Berapa ekspektasi rasio baca dan tulis?

Langkah 2: Buat rancangan tingkat tinggi

Jabarkan rancangan tingkat tinggi yang mencakup seluruh komponen penting.

  • Buat sketsa komponen utama dan hubungannya
  • Beri alasan untuk ide Anda

Langkah 3: Rancang komponen inti

Perinci setiap komponen inti. Sebagai contoh, jika Anda diminta merancang layanan penyingkat tautan, diskusikan hal-hal berikut:

  • Pembangkitan dan penyimpanan campuran (hash) dari tautan penuh
    • MD5 dan Base62
    • Tabrakan campuran (hash)
    • SQL atau NoSQL
    • Skema basis data
  • Penerjemahan tautan hasil pencampuran menjadi tautan penuh
  • API dan rancangan berbasis objek

Langkah 4: Skalakan rancangan

Kenali dan tangani kemacetan dalam batasan yang ada. Sebagai contoh, apakah diperlukan hal-hal berikut untuk menangani masalah skalabilitas?

  • Pembagi beban (Load balancer)
  • Penyekalaan mendatar (Horizontal scaling)
  • Penyinggahan (Caching)
  • Pemecahan basis data (Database sharding)

Diskusikan potensi solusi dan kompromi. Segala sesuatunya adalah hasil kompromi. Tangani kemacetan menggunakan prinsip-prinsip perancangan sistem terskala.

Kalkulasi belakang amplop

Anda mungkin diminta untuk mengestimasi dengan tangan. Aculah lampiran pada sumber daya berikut:

Sumber dan bacaan lanjutan

Periksa tautan-tautan berikut untuk lebih memahami apa yang diharapkan saat wawancara perancangan sistem:

Pertanyaan wawancara rancangan sistem beserta solusinya

Pertanyaan umum pada wawancara perancangan sistem beserta contoh diskusi, kode, dan diagram.

Solusi terhubung dengan konten di dalam folder solutions/.

Pertanyaan
Perancangan Pastebin.com (or Bit.ly) Solusi
Perancangan linimasa Twitter and pencarian (atau linimasa dan pencarian Facebook) Solusi
Perancangan perayap web Solusi
Perancangan Mint.com solusi
Perancangan struktur data untuk jejaring sosial Solusi
Perancangan gudang tanda-nilai (key-value) untuk mesin pencari Solusi
Perancangan peringkat penjualan Amazon berdasarkan fitur kategori Solusi
Perancangan sistem terskala untuk jutaan pengguna pada AWS Solusi
Tambahkan pertanyaan perancangan sistem Kontribusi

Perancangan Pastebin.com (or Bit.ly)

Lihat latihan dan solusi

Imgur

Perancangan linimasa Twitter and pencarian (atau linimasa dan pencarian Facebook)

Lihat latihan dan solusi

Imgur

Perancangan perayap web

Lihat latihan dan solusi

Imgur

Perancangan Mint.com

Lihat latihan dan solusi

Imgur

Perancangan struktur data untuk jejaring sosial

Lihat latihan dan solusi

Imgur

Perancangan gudang tanda-nilai (key-value) untuk mesin pencari

Lihat latihan dan solusi

Imgur

Perancangan peringkat penjualan Amazon berdasarkan fitur kategori

Lihat latihan dan solusi

Imgur

Perancangan sistem terskala untuk jutaan pengguna pada AWS

Lihat latihan dan solusi

Imgur

Pertanyaan wawancara rancangan berbasis objek beserta solusinya

Pertanyaan umum pada wawancara perancangan berbasis objek beserta diskusi, code, dan diagram.

Solusi terhubung dengan konten di dalam folder solutions/.

Catatan: Bagian ini dalam proses pengembangan

Pertanyaan
Perancangan peta campuran (hash map) Solusi
Perancangan singgahan yang paling jarang digunakan (LRU cache) Solusi
Perancangan pusat panggilan Solusi
Perancangan tumpukan kartu Solusi
Perancangan tempat parkir Solusi
Perancangan server obrolan Solusi
Perancangan larik melingkar (circular array) Kontribusi
Add an object-oriented design question Kontribusi

Topik perancangan sistem: Mulai dari sini

Baru mengenal perancangan sistem?

Pertama-tama, kita perlu memahami prinsip-prinsip umum, belajar apa saja prinsip-prinsip tersebut, bagaimana penggunaannya, dan kelebihan dan kekurangannya.

Langkah 1: Tonton kuliah video skalabilitas

Kuliah skalabilitas di Harvard

  • Topik yang dicakup:
    • Penyekalaan tegak lurus (vertical scaling)
    • Penyekalan mendatar (horizontal scaling)
    • Penyinggahan (caching)
    • Pembagian beban (load balancing)
    • Pereplikasian basis data (database replication)
    • Penyekatan basis data (database partitioning)

Langkah 2: Baca artikel skalabilitas

Skalabilitas untuk orang-orangan

Langkah selanjutnya

Selanjutnya kita akan melihat kompromi pada tingkat tinggi:

  • Kinerja vs skalabilitas
  • Latensi vs lewatan
  • Ketersediaan vs konsistensi

Perlu diingat bahwa segala sesuatunya adalah hasil kompromi.

Selanjutnya kita akan mempelajari lebih dalam topik-topik tertentu seperti DNS, CDNs, dan penyeimbang beban.

Kinerja vs skalabilitas

Sebuah layanan disebut terskala jika layanan tersebut menghasilkan peningkatan kinerja secara proposional terhadap pertambahan sumber daya. Secara umum, peningkatan kinerja berarti pertambahan unit kerja yang bisa diselesaikan. kemungkinan lainnya adalah kemampuan menangani unit kerja ukurannya yang lebih besar, contohnya ketika himpunan data berkembang.1

Cara lain melihat kinerja vs skalabilitas:

  • Jika layanan terkena masalah kinerja, sistem terasa lambat oleh pengguna tunggal.
  • Jika layanan terkena masalah skalabilitas, sistem terasa cepat oleh pengguna tunggal tetapi menjadi lambat ketika di bawah tekanan.

Sumber dan bacaan lanjutan

Latensi vs lewatan

Latensi adalah waktu yang diperlukan untuk melakukan suatu aksi atau mendapatkan hasil.

Lewatan adalah jumlah aksi atau hasil yang didapatkan per satuan waktu.

Secara umum, kita menargetkan lewatan yang maksimal dengan latensi yang dapat diterima.

Sumber dan bacaan lanjutan

Ketersediaan vs konsistensi

Teorema CAP


Sumber: tinjauan kembali teorema CAP

Dalam sistem komputer terdistribusi, kita hanya dapat mendukung dua dari jaminan berikut:

  • Konsistensi (Consistency) - Setiap operasi baca menerima tulisan terbaru atau error
  • Ketersediaan (Availability) - Setiap permintaan mendapat tanggapan, tanpa jaminan tanggapan tersebut berisi informasi terbaru
  • Toleransi Penyekatan (Partition Tolerance) - Sistem tetap bekerja meskipun terjadi penyekatan yang berubah-ubah karena kegagalan jaringan

Jaringan tidak dapat diandalkan sehingga kita perlu mendukung toleransi penyekatan. Kita perlu memilih kompromi perangkat lunak antara konsistensi dan ketersediaan.

CP - konsistensi dan toleransi penyekatan

Menunggu tanggapan dari mesin yang tersekat mungkin akan gagal karena kehabisan waktu. CP adalah kompromi yang baik jika bisnis mempunyai kebutuhan baca dan tulis yang bersifat atom.

AP - ketersediaan dan toleransi penyekatan

Tanggapan berisi data terakhir yang tersedia pada sebuah mesin dimana data tersebut mungkin bukan data terbaru. Tulisan bakal memerlukan beberapa saat untuk tersebar ke mesin lain ketika masalah penyekatan selesai.

AP adalah kompromi yang baik jika kebutuhan bisnis mengijinkan untuk konsistensi akan datang atau ketika sistem perlu tetap bekerja walaupun ada kesalahan pihak luar.

Sumber dan bacaan lanjutan

Pola konsistensi

Dengan adanya salinan ganda data, kita dihadapkan dengan pilihan cara untuk menyinkronkan salinan tersebut. Salinan data perlu disinkronkan sehingga pengguna memiliki pandangan yang konsisten terhadap data. Ingat kembali definisi konsistensi dari teorema CAP - Setiap operasi baca menerima tulisan terbaru atau gagal.

Konsistensi lemah

Setelah operasi tulis, operasi baca belum tentu melihat hasil operasi tulis. Pendekatan usaha terbaik perlu diambil.

Pendekatan ini bisa dilihat pada sistem contohnya Memcached. Konsistensi lemah bekerja dengan baik pada sistem kasus penggunaan waktu nyata contohnya VoIP, obrolan video, dan permainan banyak pemain waktu nyata. Sebagai contoh, jika kita dalam panggilan telpon dan kehilangan sinyal untuk beberapa detik, kita tidak akan mendengar pembicaraan yang terjadi ketika koneksi terputus.

Konsistensi akan datang (eventual consistency)

Setelah operasi tulis, operasi baca akan melihat pada waktu yang akan datang (biasanya dalam mili seconds). Data direplikasi secara asinkron.

Pendekatan ini terlihat di sistem contohnya DNS dan email. Konsistensi akan datang bekerja dengan baik di sistem yang sangat tersedia.

Konsisten kuat

Setelah operasi tulis, operasi baca akan langsung melihatnya. Data direplikasi secara sinkron.

Pendekatan ini terlihat pada sistem pemberkasan dan RDBMS. Konsistensi kuat bekerja dengan baik di sistem yang membutuhkan transaksi.

Sumber dan bacaan tambahan

Pola ketersediaan

Ada dua pola utama untuk mendukung ketersediaan tinggi: fail-over dan replikasi.

Fail-over

Aktif-pasif

Dengan mekanisme fail-over aktif-pasif, denyut nadi dikirim antara server aktif dan pasif dalam keadaan siaga. Jika pengiriman denyut nadi terinterupsi, server pasif akan mengambil alih alamat IP yang aktif dan meneruskan layanan.

Lamanya waktu penghentian ditentukan berdasarkan kondisi server pasif apakah dalam status siaga 'panas' atau siaga 'dingin'. Hanya server yang aktif yang melayani permintaan.

Fail-over aktif-pasif disebut juga dengan istilah failover master-slave.

Aktif-aktif

Dalam aktif-aktif, kedua server mengelola layanan secara berbarengan, menyebarkan beban kerja diantara mereka.

Jika server terhubung langsung ke internet, DNS perlu mengetahui IP publik kedua server. Jika server terhubung ke jaringan internal, logik pada aplikasi perlu mengetahui alamat IP kedua server.

Failover aktif-aktif disebut juga dengan istilah failover master-master.

Kerugian: failover

  • Fail-over meningkatkan jumlah perangkat keras yang dibutuhkan dan kompleksitas.
  • Ada potensi kehilangan data ketika sistem aktif gagal pada ada data terbaru yang sudah berhasil ditulis di server aktif tetapi belum direplikasi ke server pasif.

Replikasi

Master-slave and master-master

Topik ini dibahas lebih lanjut di bagian Basis data:

Ketersediaan dalam angka

Ketersediaan seringnya dinyatakan berdasarkan waktu nyala (atau waktu padam) sebagai persentasi dari waktu ketersediaan layanan. Ketersediaan umumnya diukur di dalam angka 9s. Sebuah layanan dengan tingkat ketersediaan 99.99% digambarkan sebagai layanan yang memiliki empat 9.

Ketersediaan 99.9% - tiga 9

Durasi Waktu padam yang terterima
Waktu padam per tahun 8h 45min 57s
Waktu padam per bulan 43m 49.7s
Waktu padam per minggu 10m 4.8s
Waktu padam per hari 1m 26.4s

Ketersediaan 99.99% - empat 9

Durasi Waktu padam yang terterima
Waktu padam per tahun 52min 35.7s
Waktu padam per bulan 4m 23s
Waktu padam per minggu 1m 5s
Waktu padam per hari 8.6s

Ketersediaan sejajar vs berurutan

Jika suatu layanan terdiri dari beberapa komponen yang rentan mengalami kegagalan, ketersediaan layanan secara keseluruhan tergantung apakah komponen tersebut sejajar atau berurutan.

Berurutan

Ketersedian secara keseluruhan berkurang ketika dua komponen dengan tingkat ketersediaan kurang dari 100% bekerja berurutan:

Ketersedian (Total) = Ketersediaan (Foo) * Ketersedian (Bar)

Jika Foo dan Bar keduanya masing-masing memiliki 99.9% tingkat ketersediaan, maka total tingkat ketersediaan keduanya berurutan menjadi 99.8%.

Sejajar

Ketersediaan secara keseluruhan meningkat ketika dua komponen dengan tingkat tersediaan kurang dari 100% bekerja sejajar:

Ketersediaan (Total) = 1 - (1 - Ketersediaan (Foo)) * (1 - Ketersediaan (Bar))

Jika Foo dan Bar keduanya masing-masing memiliki tingkat ketersediaan sebesar 99.9%, maka total tingkat ketersediaan sejajar keduanya adalah 99.9999%.

Domain name system


Sumber: presentasi keamanan DNS

Domain Name System (DNS) menerjemahkan nama suatu domain seperti www.example.com menjadi alamaat IP.

DNS bersifat hierarki, dengan beberapa server berkuasa di level puncak. Router atau ISP yang kita gunakan menyediakan informasi mengenai server DNS yang dihubungi ketika melakukan pencarian. Server DNS tingkat lebih rendah menyinggahkan pemetaan yang mungkin tidak mutakhir karena penundaan perambatan DNS. Hasil DNS bisa juga disinggahkan oleh peramban atau sistem operasi selama periode tertentu yang ditentukan oleh masa berlaku DNS.

  • NS record (name server) - Menentukan server DNS untuk domain/subdomain tersebut.
  • MX record (mail exchange) - Menentukan server email untuk penerimaan pesan.
  • A record (address) - Mengarahkan sebuah nama ke alamat IP.
  • CNAME (canonical) - Mengarahkan sebuah nama ke nama lain. Nama lain tersebut bisa berupa CNAME atau A (Contohnya example.com diarahkan ke www.example.com).

Layanan seperti CloudFlare dan Route 53 menyediakan layanan DNS terkelola. Beberapa layanan DNS mampu mengarahkan lalu lintas melalui berbagai metode:

  • Weighted round robin
    • Mencegah lalu lintas bergerak menuju server yang sedang dalam pemeliharaan
    • Menyeimbangkan antara berbagai ukuran gugusan
    • Pengujian A/B
  • Berdasarkan latensi
  • Berdasarkan geolokasi

Kekurangan: DNS

  • Pengaksesan server DNS menambahkan sedikit penundaan, walaupun sudah diperingan menggunakan singgahan seperti penjelasan di atas.
  • Pengelolaan server DNS bisa jadi rumit dan umumnya dikelola oleh pemerintah, penyedia jasa internet, dan perusahaan besar.
  • Layanan DNS belakangan ini mengalami serangan DDoS, preventing users from accessing websites such as Twitter without knowing Twitter's IP address(es).

Sumber dan bacaan lanjutan

Content delivery network


Sumber: Mengapa menggunakan CDN

Content delivery network (CDN) adalah jaringan server proksi yang tersebar secara global, menyuguhkan konten dari lokasi terdekat ke pengguna. Umumnya, berkas statis seperti HTML/CSS/JS, foto, dan video disuguhkan oleh CDN, meskipun beberapa CDN seperti Amazon CloudFront mendukung konten dinamis. Resolusi DNS situs akan memberitahu pengguna server mana yang dihubungi.

Penyuguhan konten melalui CDN bisa meningkatkan kinerja secara signifikan melalui dua cara:

  • Pengguna menerima konten dari pusat data terdekat ke mereka
  • Server kita tidak perlu melayani permintaan yang dipenuhi oleh CDN

CDN setor

CDN setor menerima konten baru kapanpun perubahan terjadi di server kita. Kita bertanggungjawab penuh untuk menyediakan konten, mengunggah langsung ke CDN dan menulis ulang alamat URL supaya mengarah ke CDN. Kita dapat mengkonfigurasi kapan konten kedaluarsa dan kapan konten diperbarui. Konten diunggah hanya ketika ada konten baru atau konten berubah untuk meminimalkan lalu lintas dan memaksimalkan penyimpanan.

Situs dengan lalu lintas kecil atau situs dengan konten yang tidak sering diperbarui bekerja baik dengan CDN setor. Konten ditempatkan pada CDN sekali, daripada ditarik ulang secara berkala.

CDN tarik

CDN tarik mengambil konten baru dari server kita ketika ada user pertama yang meminta konten tersebut. Kita menyimpan konten di server kita dan menulis ulang URL supaya mengarah ke CDN. Mekanisme ini menghasilkan permintaan yang lebih lambat sampai konten singgah di CDN.

Masa berlaku menentukan berapa lama konten akan singgah. CDN tarik meminimalkan ruang penyimpanan pada CDN, tetapi bisa menciptakan lalu lintas yang berlebihan jika berkas kedaluarsa dan ditarik sebelum berkas tersebut diubah.

Situs dengan lalu lintas padat bekerja baik dengan CDN tarik, sebagaimana lalu lintas tersebar secara merata dengan konten yang baru diminta saja yang tersimpan di CDN.

Kekurangan: CDN

  • Biaya CDN bisa signifikan tergantung kepadatan lalu lintas, meskipun perlu ditimbang biaya tambahan yang akan ditanggung tanpa CDN.
  • Konten mungkin kedaluarsa jika konten diupdate sebelum masa berlaku habis.
  • CDN mengharuskan perubahan alamat URL sehingga konten statis mengarah ke CDN.

Sumber dan bacaan lanjutan

Penyeimbang beban


Sumber: Pola perancangan sistem terskala

Penyeimbang beban membagikan permintaan klien yang masuk ke sumber daya komputasi seperti server aplikasi dan basis data. Dalam setiap kasus, penyeimbang beban mengembalikan tanggapan dari sumber daya komputasi ke klien yang sesuai. Penyeimbang beban efektif dalam hal:

  • Mencegah permintaan dilayani oleh server yang tidak sehat
  • Mencegah sumber daya kelebihan beban
  • Membantu menghilangkan titik kegagalan

Penyeimbang beban bisa diimplementasikan menggunakan perangkat keras (mahal) atau dengan perangkat lunak seperti HAProxy.

Yang termasuk dalam manfaat tambahan:

  • Terminasi SSL - Mendekripsi permintaan yang datang dan mengenkripsi tanggapan server sehingga server bagian belakang tidak perlu melakukan operasi yang berpotensi mahal ini
  • Persistensi sesi - Mengeluarkan kuki dan mengarahkan permintaan klien yang spesifik ke server yang sama jika aplikasi web tidak melakukan pelacakan sesi

Untuk menjaga dari kegagalan, biasa dilakukan pendirian penyeimbang beban berganda, baik itu mode aktif-pasif atau aktif-aktif.

Penyeimbang beban dapat mengarahkan lalu linta berdasarkan berbagai metrik termasuk:

Penyeimbangan beban lapisan ke-4

Penyeimbang beban lapisan ke-4 menggunakan informasi pada lapisan transportasi untuk menentukan bagaimana cara mendistribusikan permintaan. Umumnya, proses ini melibatkan asal alamat ip, tujuan alamat ip dan porta pada tajuk (header), tetapi bukan isi paket. Penyeimbang beban lapisan ke-4 meneruskan paket jaringan dari dan ke server hulu dengan melakukan translasi alamat jaringan.

Penyeimbang beban lapisan ke-7

Penyeimbang beban lapisan ke-7 menggunakan informasi pada lapisan aplikasi untuk menentukan cara mendistribusikan permintaan. Informasi yang dapat dilibat adalah tajuk, pesan, dan kuki. Penyeimbang beban lapisan ke-7 mengakhiri lalu lintas jaringan, membaca pesan, membuat keputusan penyeimbangan beban, kemudian membuka koneksi ke server terpilih. Sebagai contoh, penyeimbang beban lapisan ke-7 bisa mengarahkan lalu lintas video menuju server yang menginangi video tersebut sembari mengarahkan lalu lintas tagihan pengguna yang lebih sensitif ke server yang keamanannya sudah diperketat. Layer 7 load balancers look at the application layer to decide how to distribute requests.

Dengan mengorbankan fleksibilitas, penyeimbang beban lapisan ke-4 memerlukan waktu dan sumber daya komputasi yang lebih sedikit dibandingkan dengan penyeimbang beban lapisan ke-7, walaupun dampak kinerjanya bisa sangat minim pada perangkat keras komoditas modern.

Penyekalaan horizontal

Penyeimbang beban bisa juga membantu penyekalaan horizontal untuk meningkatkan kinerja dan ketersediaan. Penyekalaan keluar menggunakan mesin komoditas lebih efisien dari segi biaya dan menghasilkan ketersediaan yang lebih tinggi dibandingkan dengan penyekalaan ke atas server. Penyekalaan ke atas sebuah server individu menggunakan perangkat keras yang lebih mahal disebut dengan penyekalaan vertikal. Mencari pekerja yang bekerja pada perangkat lunak komoditas juga lebih mudah dibandingkan mencari pekerja untuk sistem firma terspesialisasi.

Kekurangan: Penyekalaan horizontal

  • Penyekalaan secara horizontal meningkatkan kompleksitas dan melibatkan pengklonaan server
    • Server seharusnya nirkeadaan: server seharusnya tidak mengandung data apapun yang berhubungan dengan pengguna seperti sesi dan profil pengguna
    • Sesi dapat disimpan pada penyimpanan data terpusat seperti basis data (SQL, NoSQL) atau singgahan persisten (Redis, Memcached)
  • Server hilir seperti singgahan dan basis data perlu menangani lebih banyak koneksi secara simultan ketika terjadi penyekalaan keluar server hulu

Kekurangan: penyeimbang beban

  • Penyeimbang beban bisa menjadi titik macet kinerja jika penyeimbang beban tidak memiliki sumber daya yang cukup atau tidak dikonfigurasi dengan benar.
  • Menambahkan penyeimbang beban untuk menghilangkan titik kegagalan akan meningkatkan kompleksitas.
  • Penyeimbang beban tunggal adalah titik kegagalan. Konfigurasi penyeimbang beban ganda membuat sistem lebih kompleks lagi.

Sumber dan bacaan lanjutan

Proksi terbalik (server web)


Sumber: Wikipedia

Proksi terbalik adalah server web yang memusatkan layanan-layanan internal dan menyediakan antarmuka terpadu ke publik. Permintaan dari klien diteruskan ke salah satu server yang mampu memenuhi permintaan tersebut sebelum proksi terbalik mengembalikan tanggapan server tersebut ke klien.

Benefit tambahan yang termasuk di dalamnya:

  • Meningkatkan keamanan - Menyembunyikan informasi mengenai server bagian belakang, memasukan alamat IP ke dalam daftar hitam, dan membatasi jumlah koneksi per klien
  • Meningkatkan skalabilitas dan fleksibilitas - Klien hanya melihat alamat IP proksi terbalik sehingga memungkinkan kita untuk menyekalakan server atau mengganti konfigurasi
  • Terminasi SSL - Mendekripsi permintaan yang datang dan mengenkripsi tanggapan server sehingga server bagian belakang tidak perlu melakukan operasi yang berpotensi mahal ini
  • Kompresi - Memampatkan tanggapan server
  • Singgahan - Mengembalikan tanggapan berdasarkan permintaan yang ada disinggahan
  • Konten statis - Melayani konten statis secara langsung
    • HTML/CSS/JS
    • Foto
    • Video
    • Dan lainnya

Penyeimbang beban vs proksi terbalik

  • Penggelaran penyeimbang beban berguna ketika kita mempunyai beberapa server. Seringkali, penyeimbang beban mengarahkan lalu lintas ke sekumpulan server yang melayani fungsi yang sama.
  • Proksi terbalik bisa berguna bahkan untuk satu server web atau satu server aplikasi sehingga membuka manfaat yang dijelaskan pada bagian sebelumnya.
  • Solusi seperti NGINX dan HAProxy mendukung pemproxyan terbalik dan penyeimbangan beban lapisan ke-7.

Kekurangan: proksi terbalik

  • Menambahkan proksi terbalik meningkatkan kompleksitas.
  • Proksi terbalik tunggal adalah titik kegagalan. Pengkonfigurasian proksi terbalik ganda (Contohnya failover) meningkatkan kompleksitas lagi.

Sumber dan bacaan lanjutan

Application layer


Source: Intro to architecting systems for scale

Separating out the web layer from the application layer (also known as platform layer) allows you to scale and configure both layers independently. Adding a new API results in adding application servers without necessarily adding additional web servers. The single responsibility principle advocates for small and autonomous services that work together. Small teams with small services can plan more aggressively for rapid growth.

Workers in the application layer also help enable asynchronism.

Microservices

Related to this discussion are microservices, which can be described as a suite of independently deployable, small, modular services. Each service runs a unique process and communicates through a well-defined, lightweight mechanism to serve a business goal. 1

Pinterest, for example, could have the following microservices: user profile, follower, feed, search, photo upload, etc.

Service Discovery

Systems such as Consul, Etcd, and Zookeeper can help services find each other by keeping track of registered names, addresses, and ports. Health checks help verify service integrity and are often done using an HTTP endpoint. Both Consul and Etcd have a built in key-value store that can be useful for storing config values and other shared data.

Disadvantage(s): application layer

  • Adding an application layer with loosely coupled services requires a different approach from an architectural, operations, and process viewpoint (vs a monolithic system).
  • Microservices can add complexity in terms of deployments and operations.

Source(s) and further reading

Basis data


Source: Scaling up to your first 10 million users

Relational database management system (RDBMS)

A relational database like SQL is a collection of data items organized in tables.

ACID is a set of properties of relational database transactions.

  • Atomicity - Each transaction is all or nothing
  • Consistency - Any transaction will bring the database from one valid state to another
  • Isolation - Executing transactions concurrently has the same results as if the transactions were executed serially
  • Durability - Once a transaction has been committed, it will remain so

There are many techniques to scale a relational database: master-slave replication, master-master replication, federation, sharding, denormalization, and SQL tuning.

Replikasi master-slave

The master serves reads and writes, replicating writes to one or more slaves, which serve only reads. Slaves can also replicate to additional slaves in a tree-like fashion. If the master goes offline, the system can continue to operate in read-only mode until a slave is promoted to a master or a new master is provisioned.


Source: Scalability, availability, stability, patterns

Disadvantage(s): master-slave replication
  • Additional logic is needed to promote a slave to a master.
  • See Disadvantage(s): replication for points related to both master-slave and master-master.

Replikasi master-master

Both masters serve reads and writes and coordinate with each other on writes. If either master goes down, the system can continue to operate with both reads and writes.


Source: Scalability, availability, stability, patterns

Disadvantage(s): master-master replication
  • You'll need a load balancer or you'll need to make changes to your application logic to determine where to write.
  • Most master-master systems are either loosely consistent (violating ACID) or have increased write latency due to synchronization.
  • Conflict resolution comes more into play as more write nodes are added and as latency increases.
  • See Disadvantage(s): replication for points related to both master-slave and master-master.
Disadvantage(s): replication
  • There is a potential for loss of data if the master fails before any newly written data can be replicated to other nodes.
  • Writes are replayed to the read replicas. If there are a lot of writes, the read replicas can get bogged down with replaying writes and can't do as many reads.
  • The more read slaves, the more you have to replicate, which leads to greater replication lag.
  • On some systems, writing to the master can spawn multiple threads to write in parallel, whereas read replicas only support writing sequentially with a single thread.
  • Replication adds more hardware and additional complexity.
Source(s) and further reading: replication

Federation


Source: Scaling up to your first 10 million users

Federation (or functional partitioning) splits up databases by function. For example, instead of a single, monolithic database, you could have three databases: forums, users, and products, resulting in less read and write traffic to each database and therefore less replication lag. Smaller databases result in more data that can fit in memory, which in turn results in more cache hits due to improved cache locality. With no single central master serializing writes you can write in parallel, increasing throughput.

Disadvantage(s): federation
  • Federation is not effective if your schema requires huge functions or tables.
  • You'll need to update your application logic to determine which database to read and write.
  • Joining data from two databases is more complex with a server link.
  • Federation adds more hardware and additional complexity.
Source(s) and further reading: federation

Sharding


Source: Scalability, availability, stability, patterns

Sharding distributes data across different databases such that each database can only manage a subset of the data. Taking a users database as an example, as the number of users increases, more shards are added to the cluster.

Similar to the advantages of federation, sharding results in less read and write traffic, less replication, and more cache hits. Index size is also reduced, which generally improves performance with faster queries. If one shard goes down, the other shards are still operational, although you'll want to add some form of replication to avoid data loss. Like federation, there is no single central master serializing writes, allowing you to write in parallel with increased throughput.

Common ways to shard a table of users is either through the user's last name initial or the user's geographic location.

Disadvantage(s): sharding
  • You'll need to update your application logic to work with shards, which could result in complex SQL queries.
  • Data distribution can become lopsided in a shard. For example, a set of power users on a shard could result in increased load to that shard compared to others.
    • Rebalancing adds additional complexity. A sharding function based on consistent hashing can reduce the amount of transferred data.
  • Joining data from multiple shards is more complex.
  • Sharding adds more hardware and additional complexity.
Source(s) and further reading: sharding

Denormalization

Denormalization attempts to improve read performance at the expense of some write performance. Redundant copies of the data are written in multiple tables to avoid expensive joins. Some RDBMS such as PostgreSQL and Oracle support materialized views which handle the work of storing redundant information and keeping redundant copies consistent.

Once data becomes distributed with techniques such as federation and sharding, managing joins across data centers further increases complexity. Denormalization might circumvent the need for such complex joins.

In most systems, reads can heavily outnumber writes 100:1 or even 1000:1. A read resulting in a complex database join can be very expensive, spending a significant amount of time on disk operations.

Disadvantage(s): denormalization
  • Data is duplicated.
  • Constraints can help redundant copies of information stay in sync, which increases complexity of the database design.
  • A denormalized database under heavy write load might perform worse than its normalized counterpart.
Source(s) and further reading: denormalization

SQL tuning

SQL tuning is a broad topic and many books have been written as reference.

It's important to benchmark and profile to simulate and uncover bottlenecks.

  • Benchmark - Simulate high-load situations with tools such as ab.
  • Profile - Enable tools such as the slow query log to help track performance issues.

Benchmarking and profiling might point you to the following optimizations.

Tighten up the schema
  • MySQL dumps to disk in contiguous blocks for fast access.
  • Use CHAR instead of VARCHAR for fixed-length fields.
    • CHAR effectively allows for fast, random access, whereas with VARCHAR, you must find the end of a string before moving onto the next one.
  • Use TEXT for large blocks of text such as blog posts. TEXT also allows for boolean searches. Using a TEXT field results in storing a pointer on disk that is used to locate the text block.
  • Use INT for larger numbers up to 2^32 or 4 billion.
  • Use DECIMAL for currency to avoid floating point representation errors.
  • Avoid storing large BLOBS, store the location of where to get the object instead.
  • VARCHAR(255) is the largest number of characters that can be counted in an 8 bit number, often maximizing the use of a byte in some RDBMS.
  • Set the NOT NULL constraint where applicable to improve search performance.
Use good indices
  • Columns that you are querying (SELECT, GROUP BY, ORDER BY, JOIN) could be faster with indices.
  • Indices are usually represented as self-balancing B-tree that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time.
  • Placing an index can keep the data in memory, requiring more space.
  • Writes could also be slower since the index also needs to be updated.
  • When loading large amounts of data, it might be faster to disable indices, load the data, then rebuild the indices.
Avoid expensive joins
Partition tables
  • Break up a table by putting hot spots in a separate table to help keep it in memory.
Tune the query cache
Source(s) and further reading: SQL tuning

NoSQL

NoSQL is a collection of data items represented in a key-value store, document store, wide column store, or a graph database. Data is denormalized, and joins are generally done in the application code. Most NoSQL stores lack true ACID transactions and favor eventual consistency.

BASE is often used to describe the properties of NoSQL databases. In comparison with the CAP Theorem, BASE chooses availability over consistency.

  • Basically available - the system guarantees availability.
  • Soft state - the state of the system may change over time, even without input.
  • Eventual consistency - the system will become consistent over a period of time, given that the system doesn't receive input during that period.

In addition to choosing between SQL or NoSQL, it is helpful to understand which type of NoSQL database best fits your use case(s). We'll review key-value stores, document stores, wide column stores, and graph databases in the next section.

Key-value store

Abstraction: hash table

A key-value store generally allows for O(1) reads and writes and is often backed by memory or SSD. Data stores can maintain keys in lexicographic order, allowing efficient retrieval of key ranges. Key-value stores can allow for storing of metadata with a value.

Key-value stores provide high performance and are often used for simple data models or for rapidly-changing data, such as an in-memory cache layer. Since they offer only a limited set of operations, complexity is shifted to the application layer if additional operations are needed.

A key-value store is the basis for more complex systems such as a document store, and in some cases, a graph database.

Source(s) and further reading: key-value store

Document store

Abstraction: key-value store with documents stored as values

A document store is centered around documents (XML, JSON, binary, etc), where a document stores all information for a given object. Document stores provide APIs or a query language to query based on the internal structure of the document itself. Note, many key-value stores include features for working with a value's metadata, blurring the lines between these two storage types.

Based on the underlying implementation, documents are organized by collections, tags, metadata, or directories. Although documents can be organized or grouped together, documents may have fields that are completely different from each other.

Some document stores like MongoDB and CouchDB also provide a SQL-like language to perform complex queries. DynamoDB supports both key-values and documents.

Document stores provide high flexibility and are often used for working with occasionally changing data.

Source(s) and further reading: document store

Wide column store


Source: SQL & NoSQL, a brief history

Abstraction: nested map ColumnFamily<RowKey, Columns<ColKey, Value, Timestamp>>

A wide column store's basic unit of data is a column (name/value pair). A column can be grouped in column families (analogous to a SQL table). Super column families further group column families. You can access each column independently with a row key, and columns with the same row key form a row. Each value contains a timestamp for versioning and for conflict resolution.

Google introduced Bigtable as the first wide column store, which influenced the open-source HBase often-used in the Hadoop ecosystem, and Cassandra from Facebook. Stores such as BigTable, HBase, and Cassandra maintain keys in lexicographic order, allowing efficient retrieval of selective key ranges.

Wide column stores offer high availability and high scalability. They are often used for very large data sets.

Source(s) and further reading: wide column store

Graph database


Source: Graph database

Abstraction: graph

In a graph database, each node is a record and each arc is a relationship between two nodes. Graph databases are optimized to represent complex relationships with many foreign keys or many-to-many relationships.

Graphs databases offer high performance for data models with complex relationships, such as a social network. They are relatively new and are not yet widely-used; it might be more difficult to find development tools and resources. Many graphs can only be accessed with REST APIs.

Source(s) and further reading: graph

Source(s) and further reading: NoSQL

SQL or NoSQL


Source: Transitioning from RDBMS to NoSQL

Reasons for SQL:

  • Structured data
  • Strict schema
  • Relational data
  • Need for complex joins
  • Transactions
  • Clear patterns for scaling
  • More established: developers, community, code, tools, etc
  • Lookups by index are very fast

Reasons for NoSQL:

  • Semi-structured data
  • Dynamic or flexible schema
  • Non-relational data
  • No need for complex joins
  • Store many TB (or PB) of data
  • Very data intensive workload
  • Very high throughput for IOPS

Sample data well-suited for NoSQL:

  • Rapid ingest of clickstream and log data
  • Leaderboard or scoring data
  • Temporary data, such as a shopping cart
  • Frequently accessed ('hot') tables
  • Metadata/lookup tables
Source(s) and further reading: SQL or NoSQL

Singgahan


Source: Scalable system design patterns

Caching improves page load times and can reduce the load on your servers and databases. In this model, the dispatcher will first lookup if the request has been made before and try to find the previous result to return, in order to save the actual execution.

Databases often benefit from a uniform distribution of reads and writes across its partitions. Popular items can skew the distribution, causing bottlenecks. Putting a cache in front of a database can help absorb uneven loads and spikes in traffic.

Client caching

Caches can be located on the client side (OS or browser), server side, or in a distinct cache layer.

CDN caching

CDNs are considered a type of cache.

Web server caching

Reverse proxies and caches such as Varnish can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers.

Database caching

Your database usually includes some level of caching in a default configuration, optimized for a generic use case. Tweaking these settings for specific usage patterns can further boost performance.

Application caching

In-memory caches such as Memcached and Redis are key-value stores between your application and your data storage. Since the data is held in RAM, it is much faster than typical databases where data is stored on disk. RAM is more limited than disk, so cache invalidation algorithms such as least recently used (LRU) can help invalidate 'cold' entries and keep 'hot' data in RAM.

Redis has the following additional features:

  • Persistence option
  • Built-in data structures such as sorted sets and lists

There are multiple levels you can cache that fall into two general categories: database queries and objects:

  • Row level
  • Query-level
  • Fully-formed serializable objects
  • Fully-rendered HTML

Generally, you should try to avoid file-based caching, as it makes cloning and auto-scaling more difficult.

Caching at the database query level

Whenever you query the database, hash the query as a key and store the result to the cache. This approach suffers from expiration issues:

  • Hard to delete a cached result with complex queries
  • If one piece of data changes such as a table cell, you need to delete all cached queries that might include the changed cell

Caching at the object level

See your data as an object, similar to what you do with your application code. Have your application assemble the dataset from the database into a class instance or a data structure(s):

  • Remove the object from cache if its underlying data has changed
  • Allows for asynchronous processing: workers assemble objects by consuming the latest cached object

Suggestions of what to cache:

  • User sessions
  • Fully rendered web pages
  • Activity streams
  • User graph data

When to update the cache

Since you can only store a limited amount of data in cache, you'll need to determine which cache update strategy works best for your use case.

Cache-aside


Source: From cache to in-memory data grid

The application is responsible for reading and writing from storage. The cache does not interact with storage directly. The application does the following:

  • Look for entry in cache, resulting in a cache miss
  • Load entry from the database
  • Add entry to cache
  • Return entry
def get_user(self, user_id):
    user = cache.get("user.{0}", user_id)
    if user is None:
        user = db.query("SELECT * FROM users WHERE user_id = {0}", user_id)
        if user is not None:
            key = "user.{0}".format(user_id)
            cache.set(key, json.dumps(user))
    return user

Memcached is generally used in this manner.

Subsequent reads of data added to cache are fast. Cache-aside is also referred to as lazy loading. Only requested data is cached, which avoids filling up the cache with data that isn't requested.

Disadvantage(s): cache-aside
  • Each cache miss results in three trips, which can cause a noticeable delay.
  • Data can become stale if it is updated in the database. This issue is mitigated by setting a time-to-live (TTL) which forces an update of the cache entry, or by using write-through.
  • When a node fails, it is replaced by a new, empty node, increasing latency.

Write-through


Source: Scalability, availability, stability, patterns

The application uses the cache as the main data store, reading and writing data to it, while the cache is responsible for reading and writing to the database:

  • Application adds/updates entry in cache
  • Cache synchronously writes entry to data store
  • Return

Application code:

set_user(12345, {"foo":"bar"})

Cache code:

def set_user(user_id, values):
    user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
    cache.set(user_id, user)

Write-through is a slow overall operation due to the write operation, but subsequent reads of just written data are fast. Users are generally more tolerant of latency when updating data than reading data. Data in the cache is not stale.

Disadvantage(s): write through
  • When a new node is created due to failure or scaling, the new node will not cache entries until the entry is updated in the database. Cache-aside in conjunction with write through can mitigate this issue.
  • Most data written might never be read, which can be minimized with a TTL.

Write-behind (write-back)


Source: Scalability, availability, stability, patterns

In write-behind, the application does the following:

  • Add/update entry in cache
  • Asynchronously write entry to the data store, improving write performance
Disadvantage(s): write-behind
  • There could be data loss if the cache goes down prior to its contents hitting the data store.
  • It is more complex to implement write-behind than it is to implement cache-aside or write-through.

Refresh-ahead


Source: From cache to in-memory data grid

You can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration.

Refresh-ahead can result in reduced latency vs read-through if the cache can accurately predict which items are likely to be needed in the future.

Disadvantage(s): refresh-ahead
  • Not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh-ahead.

Disadvantage(s): cache

  • Need to maintain consistency between caches and the source of truth such as the database through cache invalidation.
  • Cache invalidation is a difficult problem, there is additional complexity associated with when to update the cache.
  • Need to make application changes such as adding Redis or memcached.

Source(s) and further reading

Asynchronism


Source: Intro to architecting systems for scale

Asynchronous workflows help reduce request times for expensive operations that would otherwise be performed in-line. They can also help by doing time-consuming work in advance, such as periodic aggregation of data.

Message queues

Message queues receive, hold, and deliver messages. If an operation is too slow to perform inline, you can use a message queue with the following workflow:

  • An application publishes a job to the queue, then notifies the user of job status
  • A worker picks up the job from the queue, processes it, then signals the job is complete

The user is not blocked and the job is processed in the background. During this time, the client might optionally do a small amount of processing to make it seem like the task has completed. For example, if posting a tweet, the tweet could be instantly posted to your timeline, but it could take some time before your tweet is actually delivered to all of your followers.

Redis is useful as a simple message broker but messages can be lost.

RabbitMQ is popular but requires you to adapt to the 'AMQP' protocol and manage your own nodes.

Amazon SQS is hosted but can have high latency and has the possibility of messages being delivered twice.

Task queues

Tasks queues receive tasks and their related data, runs them, then delivers their results. They can support scheduling and can be used to run computationally-intensive jobs in the background.

Celery has support for scheduling and primarily has python support.

Back pressure

If queues start to grow significantly, the queue size can become larger than memory, resulting in cache misses, disk reads, and even slower performance. Back pressure can help by limiting the queue size, thereby maintaining a high throughput rate and good response times for jobs already in the queue. Once the queue fills up, clients get a server busy or HTTP 503 status code to try again later. Clients can retry the request at a later time, perhaps with exponential backoff.

Disadvantage(s): asynchronism

  • Use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations, as introducing queues can add delays and complexity.

Source(s) and further reading

Komunikasi


Source: OSI 7 layer model

Hypertext transfer protocol (HTTP)

HTTP is a method for encoding and transporting data between a client and a server. It is a request/response protocol: clients issue requests and servers issue responses with relevant content and completion status info about the request. HTTP is self-contained, allowing requests and responses to flow through many intermediate routers and servers that perform load balancing, caching, encryption, and compression.

A basic HTTP request consists of a verb (method) and a resource (endpoint). Below are common HTTP verbs:

Verb Description Idempotent* Safe Cacheable
GET Reads a resource Yes Yes Yes
POST Creates a resource or trigger a process that handles data No No Yes if response contains freshness info
PUT Creates or replace a resource Yes No No
PATCH Partially updates a resource No No Yes if response contains freshness info
DELETE Deletes a resource Yes No No

*Can be called many times without different outcomes.

HTTP is an application layer protocol relying on lower-level protocols such as TCP and UDP.

Source(s) and further reading: HTTP

Transmission control protocol (TCP)


Source: How to make a multiplayer game

TCP is a connection-oriented protocol over an IP network. Connection is established and terminated using a handshake. All packets sent are guaranteed to reach the destination in the original order and without corruption through:

If the sender does not receive a correct response, it will resend the packets. If there are multiple timeouts, the connection is dropped. TCP also implements flow control and congestion control. These guarantees cause delays and generally result in less efficient transmission than UDP.

To ensure high throughput, web servers can keep a large number of TCP connections open, resulting in high memory usage. It can be expensive to have a large number of open connections between web server threads and say, a memcached server. Connection pooling can help in addition to switching to UDP where applicable.

TCP is useful for applications that require high reliability but are less time critical. Some examples include web servers, database info, SMTP, FTP, and SSH.

Use TCP over UDP when:

  • You need all of the data to arrive intact
  • You want to automatically make a best estimate use of the network throughput

User datagram protocol (UDP)


Source: How to make a multiplayer game

UDP is connectionless. Datagrams (analogous to packets) are guaranteed only at the datagram level. Datagrams might reach their destination out of order or not at all. UDP does not support congestion control. Without the guarantees that TCP support, UDP is generally more efficient.

UDP can broadcast, sending datagrams to all devices on the subnet. This is useful with DHCP because the client has not yet received an IP address, thus preventing a way for TCP to stream without the IP address.

UDP is less reliable but works well in real time use cases such as VoIP, video chat, streaming, and realtime multiplayer games.

Use UDP over TCP when:

  • You need the lowest latency
  • Late data is worse than loss of data
  • You want to implement your own error correction

Source(s) and further reading: TCP and UDP

Remote procedure call (RPC)


Source: Crack the system design interview

In an RPC, a client causes a procedure to execute on a different address space, usually a remote server. The procedure is coded as if it were a local procedure call, abstracting away the details of how to communicate with the server from the client program. Remote calls are usually slower and less reliable than local calls so it is helpful to distinguish RPC calls from local calls. Popular RPC frameworks include Protobuf, Thrift, and Avro.

RPC is a request-response protocol:

  • Client program - Calls the client stub procedure. The parameters are pushed onto the stack like a local procedure call.
  • Client stub procedure - Marshals (packs) procedure id and arguments into a request message.
  • Client communication module - OS sends the message from the client to the server.
  • Server communication module - OS passes the incoming packets to the server stub procedure.
  • Server stub procedure - Unmarshalls the results, calls the server procedure matching the procedure id and passes the given arguments.
  • The server response repeats the steps above in reverse order.

Sample RPC calls:

GET /someoperation?data=anId

POST /anotheroperation
{
  "data":"anId";
  "anotherdata": "another value"
}

RPC is focused on exposing behaviors. RPCs are often used for performance reasons with internal communications, as you can hand-craft native calls to better fit your use cases.

Choose a native library (aka SDK) when:

  • You know your target platform.
  • You want to control how your "logic" is accessed.
  • You want to control how error control happens off your library.
  • Performance and end user experience is your primary concern.

HTTP APIs following REST tend to be used more often for public APIs.

Disadvantage(s): RPC

  • RPC clients become tightly coupled to the service implementation.
  • A new API must be defined for every new operation or use case.
  • It can be difficult to debug RPC.
  • You might not be able to leverage existing technologies out of the box. For example, it might require additional effort to ensure RPC calls are properly cached on caching servers such as Squid.

Representational state transfer (REST)

REST is an architectural style enforcing a client/server model where the client acts on a set of resources managed by the server. The server provides a representation of resources and actions that can either manipulate or get a new representation of resources. All communication must be stateless and cacheable.

There are four qualities of a RESTful interface:

  • Identify resources (URI in HTTP) - use the same URI regardless of any operation.
  • Change with representations (Verbs in HTTP) - use verbs, headers, and body.
  • Self-descriptive error message (status response in HTTP) - Use status codes, don't reinvent the wheel.
  • HATEOAS (HTML interface for HTTP) - your web service should be fully accessible in a browser.

Sample REST calls:

GET /someresources/anId

PUT /someresources/anId
{"anotherdata": "another value"}

REST is focused on exposing data. It minimizes the coupling between client/server and is often used for public HTTP APIs. REST uses a more generic and uniform method of exposing resources through URIs, representation through headers, and actions through verbs such as GET, POST, PUT, DELETE, and PATCH. Being stateless, REST is great for horizontal scaling and partitioning.

Disadvantage(s): REST

  • With REST being focused on exposing data, it might not be a good fit if resources are not naturally organized or accessed in a simple hierarchy. For example, returning all updated records from the past hour matching a particular set of events is not easily expressed as a path. With REST, it is likely to be implemented with a combination of URI path, query parameters, and possibly the request body.
  • REST typically relies on a few verbs (GET, POST, PUT, DELETE, and PATCH) which sometimes doesn't fit your use case. For example, moving expired documents to the archive folder might not cleanly fit within these verbs.
  • Fetching complicated resources with nested hierarchies requires multiple round trips between the client and server to render single views, e.g. fetching content of a blog entry and the comments on that entry. For mobile applications operating in variable network conditions, these multiple roundtrips are highly undesirable.
  • Over time, more fields might be added to an API response and older clients will receive all new data fields, even those that they do not need, as a result, it bloats the payload size and leads to larger latencies.

RPC and REST calls comparison

Operation RPC REST
Signup POST /signup POST /persons
Resign POST /resign
{
"personid": "1234"
}		 DELETE /persons/1234
Read a person GET /readPerson?personid=1234 GET /persons/1234
Read a person’s items list GET /readUsersItemsList?personid=1234 GET /persons/1234/items
Add an item to a person’s items POST /addItemToUsersItemsList
{
"personid": "1234";
"itemid": "456"
}		 POST /persons/1234/items
{
"itemid": "456"
}
Update an item POST /modifyItem
{
"itemid": "456";
"key": "value"
}		 PUT /items/456
{
"key": "value"
}
Delete an item POST /removeItem
{
"itemid": "456"
}		 DELETE /items/456

Source: Do you really know why you prefer REST over RPC

Source(s) and further reading: REST and RPC

Security

This section could use some updates. Consider contributing!

Security is a broad topic. Unless you have considerable experience, a security background, or are applying for a position that requires knowledge of security, you probably won't need to know more than the basics:

  • Encrypt in transit and at rest.
  • Sanitize all user inputs or any input parameters exposed to user to prevent XSS and SQL injection.
  • Use parameterized queries to prevent SQL injection.
  • Use the principle of least privilege.

Source(s) and further reading

Appendix

You'll sometimes be asked to do 'back-of-the-envelope' estimates. For example, you might need to determine how long it will take to generate 100 image thumbnails from disk or how much memory a data structure will take. The Powers of two table and Latency numbers every programmer should know are handy references.

Tabel Perpangkatan dua

Power           Exact Value         Approx Value        Bytes
---------------------------------------------------------------
7                             128
8                             256
10                           1024   1 thousand           1 KB
16                         65,536                       64 KB
20                      1,048,576   1 million            1 MB
30                  1,073,741,824   1 billion            1 GB
32                  4,294,967,296                        4 GB
40              1,099,511,627,776   1 trillion           1 TB

Source(s) and further reading

Nilai latensi yang perlu diketahui oleh setiap pemrogram]

Latency Comparison Numbers
--------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy            10,000   ns       10 us
Send 1 KB bytes over 1 Gbps network     10,000   ns       10 us
Read 4 KB randomly from SSD*           150,000   ns      150 us          ~1GB/sec SSD
Read 1 MB sequentially from memory     250,000   ns      250 us
Round trip within same datacenter      500,000   ns      500 us
Read 1 MB sequentially from SSD*     1,000,000   ns    1,000 us    1 ms  ~1GB/sec SSD, 4X memory
Disk seek                           10,000,000   ns   10,000 us   10 ms  20x datacenter roundtrip
Read 1 MB sequentially from 1 Gbps  10,000,000   ns   10,000 us   10 ms  40x memory, 10X SSD
Read 1 MB sequentially from disk    30,000,000   ns   30,000 us   30 ms 120x memory, 30X SSD
Send packet CA->Netherlands->CA    150,000,000   ns  150,000 us  150 ms

Notes
-----
1 ns = 10^-9 seconds
1 us = 10^-6 seconds = 1,000 ns
1 ms = 10^-3 seconds = 1,000 us = 1,000,000 ns

Handy metrics based on numbers above:

  • Read sequentially from disk at 30 MB/s
  • Read sequentially from 1 Gbps Ethernet at 100 MB/s
  • Read sequentially from SSD at 1 GB/s
  • Read sequentially from main memory at 4 GB/s
  • 6-7 world-wide round trips per second
  • 2,000 round trips per second within a data center

Latency numbers visualized

Source(s) and further reading

Tambahan pertanyaan wawancara rancangan sistem

Common system design interview questions, with links to resources on how to solve each.

Question Reference(s)
Design a file sync service like Dropbox youtube.com
Design a search engine like Google queue.acm.org
stackexchange.com
ardendertat.com
stanford.edu
Design a scalable web crawler like Google quora.com
Design Google docs code.google.com
neil.fraser.name
Design a key-value store like Redis slideshare.net
Design a cache system like Memcached slideshare.net
Design a recommendation system like Amazon's hulu.com
ijcai13.org
Design a tinyurl system like Bitly n00tc0d3r.blogspot.com
Design a chat app like WhatsApp highscalability.com
Design a picture sharing system like Instagram highscalability.com
highscalability.com
Design the Facebook news feed function quora.com
quora.com
slideshare.net
Design the Facebook timeline function facebook.com
highscalability.com
Design the Facebook chat function erlang-factory.com
facebook.com
Design a graph search function like Facebook's facebook.com
facebook.com
facebook.com
Design a content delivery network like CloudFlare figshare.com
Design a trending topic system like Twitter's michael-noll.com
snikolov .wordpress.com
Design a random ID generation system blog.twitter.com
github.com
Return the top k requests during a time interval cs.ucsb.edu
wpi.edu
Design a system that serves data from multiple data centers highscalability.com
Design an online multiplayer card game indieflashblog.com
buildnewgames.com
Design a garbage collection system stuffwithstuff.com
washington.edu
Design an API rate limiter https://stripe.com/blog/
Add a system design question Contribute

Arsitektu dunia nyata

Articles on how real world systems are designed.


Source: Twitter timelines at scale

Don't focus on nitty gritty details for the following articles, instead:

  • Identify shared principles, common technologies, and patterns within these articles
  • Study what problems are solved by each component, where it works, where it doesn't
  • Review the lessons learned
Type System Reference(s)
Data processing MapReduce - Distributed data processing from Google research.google.com
Data processing Spark - Distributed data processing from Databricks slideshare.net
Data processing Storm - Distributed data processing from Twitter slideshare.net
Data store Bigtable - Distributed column-oriented database from Google harvard.edu
Data store HBase - Open source implementation of Bigtable slideshare.net
Data store Cassandra - Distributed column-oriented database from Facebook slideshare.net
Data store DynamoDB - Document-oriented database from Amazon harvard.edu
Data store MongoDB - Document-oriented database slideshare.net
Data store Spanner - Globally-distributed database from Google research.google.com
Data store Memcached - Distributed memory caching system slideshare.net
Data store Redis - Distributed memory caching system with persistence and value types slideshare.net
File system Google File System (GFS) - Distributed file system research.google.com
File system Hadoop File System (HDFS) - Open source implementation of GFS apache.org
Misc Chubby - Lock service for loosely-coupled distributed systems from Google research.google.com
Misc Dapper - Distributed systems tracing infrastructure research.google.com
Misc Kafka - Pub/sub message queue from LinkedIn slideshare.net
Misc Zookeeper - Centralized infrastructure and services enabling synchronization slideshare.net
Add an architecture Contribute

Company architectures

Company Reference(s)
Amazon Amazon architecture
Cinchcast Producing 1,500 hours of audio every day
DataSift Realtime datamining At 120,000 tweets per second
DropBox How we've scaled Dropbox
ESPN Operating At 100,000 duh nuh nuhs per second
Google Google architecture
Instagram 14 million users, terabytes of photos
What powers Instagram
Justin.tv Justin.Tv's live video broadcasting architecture
Facebook Scaling memcached at Facebook
TAO: Facebook’s distributed data store for the social graph
Facebook’s photo storage
How Facebook Live Streams To 800,000 Simultaneous Viewers
Flickr Flickr architecture
Mailbox From 0 to one million users in 6 weeks
Netflix A 360 Degree View Of The Entire Netflix Stack
Netflix: What Happens When You Press Play?
Pinterest From 0 To 10s of billions of page views a month
18 million visitors, 10x growth, 12 employees
Playfish 50 million monthly users and growing
PlentyOfFish PlentyOfFish architecture
Salesforce How they handle 1.3 billion transactions a day
Stack Overflow Stack Overflow architecture
TripAdvisor 40M visitors, 200M dynamic page views, 30TB data
Tumblr 15 billion page views a month
Twitter Making Twitter 10000 percent faster
Storing 250 million tweets a day using MySQL
150M active users, 300K QPS, a 22 MB/S firehose
Timelines at scale
Big and small data at Twitter
Operations at Twitter: scaling beyond 100 million users
How Twitter Handles 3,000 Images Per Second
Uber How Uber scales their real-time market platform
Lessons Learned From Scaling Uber To 2000 Engineers, 1000 Services, And 8000 Git Repositories
WhatsApp The WhatsApp architecture Facebook bought for $19 billion
YouTube YouTube scalability
YouTube architecture

Blog teknik perusahaan

Architectures for companies you are interviewing with.

Questions you encounter might be from the same domain.

Source(s) and further reading

Looking to add a blog? To avoid duplicating work, consider adding your company blog to the following repo:

Dalam pengembangan

Interested in adding a section or helping complete one in-progress? Contribute!

  • Distributed computing with MapReduce
  • Consistent hashing
  • Scatter gather
  • Contribute

Credits

Credits and sources are provided throughout this repo.

Special thanks to:

Contact info

Feel free to contact me to discuss any issues, questions, or comments.

My contact info can be found on my GitHub page.

License

I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).

Copyright 2017 Donne Martin

Creative Commons Attribution 4.0 International License (CC BY 4.0)

http://creativecommons.org/licenses/by/4.0/