SEMINAR ON GOOGLE APP ENGINE

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Google App Engine:

Google App Engine Submitted To: Submitted By: Mrs. Debaswpna Mishra Mr. Jyotiprakash Das Asst. Prof.,CSA Dept. Roll No-58MCA/11 MCA 5 th Semester Orissa University of Agriculture & Technology, Bhubaneswar

Google App Engine:

2 Google App Engine Does one thing well: running web apps Simple app configuration Scalable Secure

App Engine Does One Thing Well:

3 App Engine Does One Thing Well App Engine handles HTTP(S) requests, nothing else Think RPC: request in, processing, response out Works well for the web and AJAX; also for other services App configuration is dead simple No performance tuning needed Everything is built to scale “infinite” number of apps, requests/sec, storage capacity APIs are simple, stupid

App Engine Architecture:

App Engine Architecture 4 Python VM process stdlib app memcache datastore mail images urlfech stateful APIs stateless APIs R/O FS req/resp

Scaling:

Scaling Low-usage apps: many apps per physical host High-usage apps: multiple physical hosts per app Stateless APIs are trivial to replicate Memcache is trivial to shard Datastore built on top of Bigtable; designed to scale well Abstraction on top of Bigtable API influenced by scalability No joins Recommendations: denormalize schema; precompute joins 5

Security:

Security Prevent the bad guys from breaking (into) your app Constrain direct OS functionality no processes, threads, dynamic library loading no sockets (use urlfetch API) can’t write files (use datastore) disallow unsafe Python extensions (e.g. ctypes) Limit resource usage Limit 1000 files per app, each at most 1MB Hard time limit of 10 seconds per request Most requests must use less than 300 msec CPU time Hard limit of 1MB on request/response size, API call size, etc. Quota system for number of requests, API calls, emails sent, etc 6

Why Not LAMP?:

Why Not LAMP? Linux, Apache, MySQL/PostgreSQL, Python/Perl/PHP/Ruby LAMP is the industry standard But management is a hassle: Configuration, tuning Backup and recovery, disk space management Hardware failures, system crashes Software updates, security patches Log rotation, cron jobs, and much more Redesign needed once your database exceeds one box “We carry pagers so you don’t have to” 7

Automatic Scaling to Application Needs:

Automatic Scaling to Application Needs You don’t need to configure your resource needs One CPU can handle many requests per second Apps are hashed (really mapped) onto CPUs: One process per app, many apps per CPU Creating a new process is a matter of cloning a generic “model” process and then loading the application code (in fact the clones are pre-created and sit in a queue) The process hangs around to handle more requests (reuse) Eventually old processes are killed (recycle) Busy apps (many QPS) get assigned to multiple CPUs This automatically adapts to the need as long as CPUs are available 8

Preserving Fairness Through Quotas:

Preserving Fairness Through Quotas Everything an app does is limited by quotas, for example: request count, bandwidth used, CPU usage, datastore call count, disk space used, emails sent, even errors! If you run out of quota that particular operation is blocked (raising an exception) for a while (~10 min) until replenished Free quotas are tuned so that a well-written app (light CPU/datastore use) can survive a moderate “slashdotting” The point of quotas is to be able to support a very large number of small apps (analogy: baggage limit in air travel) Large apps need raised quotas currently this is a manual process (search FAQ for “quota”) in the future you can buy more resources 9

Hierarchical Datastore:

Hierarchical Datastore Entities have a Kind, a Key, and Properties Entity ~~ Record ~~ Python dict ~~ Python class instance Key ~~ structured foreign key; includes Kind Kind ~~ Table ~~ Python class Property ~~ Column or Field; has a type Dynamically typed: Property types are recorded per Entity Key has either id or name the id is auto-assigned; alternatively, the name is set by app A key can be a path including the parent key, and so on Paths define entity groups which limit transactions A transaction locks the root entity (parentless ancestor key) 10

Indexes:

Indexes Properties are automatically indexed by type+value There is an index for each Kind / property name combo Whenever an entity is written all relevant indexes are updated However Blob and Text properties are never indexed This supports basic queries: AND on property equality For more advanced query needs, create composite indexes SDK auto-updates index.yaml based on queries executed These support inequalities (<, <=, >, >=) and result ordering Index building has to scan all entities due to parent keys For more info, see video of Ryan Barrett’s talk at Google I/O 11

The Future:

The Future Big things we’re working on: Large file uploads and downloads Datastore import and export for large volumes Pay-as-you-go billing (for resource usage over free quota) More languages (no I’m not telling…) Uptime monitoring site No published timeline – agile development process 12

Switch to Live Demo Now:

Switch to Live Demo Now 13

Q & A:

Q & A Anything goes 14

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