PostgreSQL as an Alternative to MSSQL
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PostgreSQL is a powerful, open-source object-relational database system with a strong reputation and extensive feature set, including full ACID compliance, multi-version concurrency control, and advanced data types like JSON and XML. It is highly customizable, supports complex queries, and is capable of handling large-scale data applications, making it a viable alternative to MSSQL. The system benefits from a large community, robust enterprise support, and continuous development, solidifying its position as a competitive database solution.
PostgreSQL as an Alternative to MSSQL
- 1. Alexei Krasner Nov 2015 PostgreSQL as MSSQL Alternative
- 2. What is PostgreSQL ▪ Powerful, open source object-relational database system. ▪ 15 years of active development and strong reputation. ▪ Runs on all major operating systems (Linux, Unix, Mac OS, Windows…). ▪ Enterprise class database. ▪ Large and responsive community. ▪ Winner of the 2015 Database Trends and Applications Readers Choice: – The most advanced open source database. – Best relational database.
- 3. Lets Start With Standards ▪ Fully ACID compliant. ▪ Includes most of SQL:2008 data types along with storage of binary objects. ▪ Conforms to the ANSI-SQL:2008 standard: – Full support for subqueries (including sub-selects). – Read-Committed and serializable transaction isolation levels. – Full support for Primary keys, Foreign Keys, Joins, Views, Triggers, Stored Procedures, Restrictions (check, unique and not null) and Cascading. – Fully relational system catalog – multiple schema per database. ▪ Native programming interfaces: Java, .NET, C/C++, Perl, Python, ODBC
- 4. Continue With a Little of Splurging ▪ Multi-Version Concurrency Control (MVCC). ▪ Asynchronous Replication, Load Balancing and Online/Hot Backups with Point in Time Recovery. ▪ Write Ahead Logging – fault tolerance. ▪ Performance: – Sophisticated Query Planner/Optimizer. – Compound, Unique, Partial and functional indexes. ▪ Supports: – International character sets, multi-byte encodings, Unicode, locale awareness. – Built-in Types – Geospatial, XML, JSONJSONB, Ranges and Arrays! – NoSQL – Key-Value store with incredible performance and Full Text Search. ▪ Highly customizable and extensible.
- 5. Before We Dive – Generalized Search Tree (GiST) ▪ Advanced indexing system – different sorting and searching algorithms: – B-tree, B+-tree, R-tree, Partial Sum trees, ranked B+-trees etc. – API for creating custom data types and extensible query methods for search. ▪ Decide WHAT to persist, HOW to persist and a way to SEARCH for it. ▪ Exceeds the general search algorithms using standard BR- trees. ▪ Foundation for many public projects – OpenFTS and PostGIS
- 6. Features Deep Dive ▪ MVCC ▪ Partitioning ▪ Useful Data Types – Date and Time – Interval – Array – Ranges – JSON – HSTORE – XML ▪ PostGIS – Geographic ▪ Full Text Search ▪ Server Side Programming ▪ Backup and Restore ▪ High Availability, Load Balancing and Replication – Sharding ▪ Big Data Readiness
- 7. Multi Version Concurrency Control - MVCC ▪ Reads should never block writes and vice versa. ▪ Each transaction sees a snapshot of data (version). – Protection from viewing inconsistency – transaction isolation. ▪ Avoidance of explicit locking solutions – minimize lock contention. ▪ TableRow level locking mechanism is still available – although proper MVCC usage will provide performance benefits.
- 8. Partitioning – Table Inheritance ▪ Support of basic table partitioning via the table inheritance concept. – Includes known partitioning benefits: ▪ Improved heavy load query performance (on a single partition). ▪ Sequential scan of a partition instead of index usage. ▪ Bulk loads and deletes accomplished by adding or removing partitions. ▪ Infrequent data can be migrated to a cheaperslower storage solution. – Range Partitioning: ▪ Table partitioned into “ranges” defined by a singleset key column (e.g. dates). – List Partitioning: ▪ Table partitioned into a list of discrete values as partitioning keys. – Hundred partitions is an acceptable limit, thousands of partitions will crucially harm performance.
- 9. Useful Data Types ▪ Date and Time – Date, Time, TimeStamp and TimeStamp with zone. – Converted to and from Unix time. – Supports the INTERVAL type. – Very convenient casting and conversion to text. – Performance wise searching and sorting algorithms (including zoneoffset). ▪ INTERVAL – representation of a period of time. – Possible negative interval values (e.g. year ago). – Intuitive arithmetic and persistence of time durations – Easy casting and converting to relevant types. – Performance wise searching and sorting algorithms on intervals.
- 10. Useful Data Types Cont. ▪ Array – supported as first-class datatype (actual field in a table). – Contain any datatype (sub arrays too). – Parameters to functions as an array. – Usages – Functions results, aggregations, getset array of data infrom the application. ▪ Range – Supported as first-class datatype. – Put range on TIME, INT or NUMERIC as a single data value. – Possible dedicated indexes to support queries utilizing ranges. – Exposed methods to define custom ranges.
- 11. Useful Data Types Cont. ▪ JSON – full support along with large dedicated set of utility functions. – Known JSONJSONB benefits – data transfer and integration standard. – Transformation fromto types and tables. – Retrieval and construction of JSON data. – Parsing, casting and conversion. ▪ HSTORE – Fast key-value store as a datatype. – NoSQL capabilities – flexibility of schema-less data store. – Still ACID compliant. – Interchange data between JSON and HSTORE.
- 12. Useful Data Types Cont. ▪ XML – Supported as a first-class datatype. – Check well formedness + type-safe operations. – Querying using Xpath. – Producing XML content, Predicates, Processing, Mapping tables to XML etc.
- 13. PostGIS ▪ Fully featured, reliable geospatial database project base on GiST (Following ISO OGC) ▪ SQL types and functions to manage vector geometries (spatial data). ▪ Capabilities: – Support for three dimensional data. – Support for geospatial formats (KML, GeoJSON) – Processing and analytics functions for vector and raster data. – Map “rastering” and geo queries. – Geo searches and reverse geo searches. ▪ Huge popularity and respect extension module – compered to ArcGIS
- 14. Full Text Search ▪ Online indexing of data and relevance ranking for database searches. ▪ Good Enough: – Stemming – Ranking – Multilingual – Fuzzy searches (misspelling) Accent.
- 15. Server Side Programming ▪ Super Extensible – functions, data types, procedural languages, operators, aggregates etc. – Embedding Functions and Stored Procedures using procedural – PL/pgSQL, PL/Tcl, PL/Perl, PL/Python ▪ Triggers – tables, views and foreign tables. ▪ Event Triggers – database global trigger. ▪ Rule System – Query modification based on given rules.
- 16. Backup and Restore ▪ Extremely flexible dump utility – migration, replication and backups becomes more reliable, controllable and configurable. – Compressed format or plain SQL (human readable). – Single table or whole database cluster. ▪ Approaches: – SQL Dump – file with generated SQL commands. On restore the backed up commands will be replayed. – File system level backup – direct copy of PostgreSQL data files. Restore will include reattaching the data files. – Continuous archiving – backing up Write Ahead Log (WAL) files. On restore log commands will be replayed.
- 17. High Availability, Load Balancing and Replication Feature Shared Disk Failover File System Replication Transaction Log Shipping Trigger-Based Master-Standby Replication Statement-Based Replication Middleware Asynchronous Multimaster Replication Synchronous Multimaster Replication Most Common Implementation NAS DRBD Streaming Repl. Slony pgpool-II Bucardo Communication Method shared disk disk blocks WAL table rows SQL table rows table rows and row locks No special hardware required X X X X X X Allows multiple master servers X X X No master server overhead X X X No waiting for multiple servers X with sync off X X Master failure will never lose data X X with sync on X X Standby accept read-only queries with hot X X X X Per-table granularity X X X No conflict resolution necessary X X X X X
- 18. Sharding and Replication ▪ Pure Sharding: – pg_shard – popular sharding extension for PostgreSQL. ▪ Running on Linux! – BDR/UDR Project – Bi-Directional Replication which adds multi-master replication to PostgreSQL. ▪ Running on Linux! Migration to windows only in a non-near future. ▪ Forked of the main PostgreSQL source. – Postgres-XL – all purpose fully ACID open source scale-out db solution. ▪ Running on Linux! ▪ Forked of the main PostgreSQL source.
- 19. Sharding and Replication Cont. ▪ Via Replication: – Hot Standby – Reducing read loads from Master to slaves (horizontal scale). – Streaming (or Bucardo, or other possible option) replication to slaves. – Load balancing “write” queries to Master, “read” queries to slaves.
- 20. PostgreSQL and Big Data ▪ PostgreSQL was used a decade before Hadoop launched, for large data volumes and complex analytics (as the only pure open source). ▪ Today heavily used in mid-sized warehouses and data-marts (1-10 TB). ▪ Source of code for many big data systems: – Netezza (IBM). – Greenplum (Pivotal) – Open Source Massively Parallel Data Warehouse. – PipelineDB – open source, run SQL queries continuously on streaming data. – EnterpriseDB and CitusDB (commercial license) – fully scaled out Postgres. – Redshift (Amazon). ▪ PostgreSQL project continuously provide new features and better performance to support big data usage.
- 21. PostgreSQL and Big Data – Features ▪ Serious NoSQL database competitor. – JSONB advanced features and ongoing massive development plan . – Extensions that provide NoSQL like API. ▪ Faster Sorts – text and long numeric sorting improvements. ▪ TABLESAMPLE – result set of pseudo-random number of rows to provide a data glimpse for further analysis. ▪ Cubes, Rollups and Grouping Sets – summarizing and exploring huge data sets in the OLAP way. ▪ BRIN indexes – much faster, suits for TBs size tables on incrementally increasing value fields (like timestamps or integers).
- 22. PostgreSQL and Big Data – Features Cont. ▪ Foreign Data Wrappers – linking external data (for querying like local) for hybrid solutions. – Foreign schema import. – JOIN pushdowns ▪ Vacuum (garbage collection – deleting) – became parallel with multi-process mode (maintaining several large tables at once). ▪ Scaling UP – Multicore scalability improvements.
- 23. Enterprise Wise ▪ Open Source ▪ Reliability ▪ Authentication ▪ Logging ▪ Documentation ▪ Support ▪ Maintenance
- 24. Open Source ▪ Available under the open source license – PostgreSQL License. ▪ Using, modifying and distributing in any openclose form. ▪ Extending and patching the relational database per projectclient etc. ▪ Variety of modules, extensions and tools based on its open source license.
- 25. Reliability ▪ PostgreSQL is relatively bug-free (compared to MSSQL). ▪ Very large community reporting, fixingworkarounds bugs. ▪ Constantly growing community
- 26. Authentication ▪ Trust Authentication. ▪ Password Authentication. ▪ GSSAPISSPI Authentication – using Kerberos. ▪ Ident Authentication. ▪ Peer Authentication. ▪ LDAP Authentication ▪ RADIUS Authentication. ▪ Certificate Authentication. ▪ Pluggable Authentication Modules.
- 27. Logging ▪ Logs in one place. – Unlike MSSQL – error logs, event log, profiler log, agent log… ▪ Easily configurable logging level. ▪ Easily redirect to CSV files and shipped to tables. ▪ Easily redirect to System Log, Windows Event Log. ▪ Logs are human readable with a great sysadmin value.
- 28. Documentation ▪ There is nothing more to add than a link: http://www.postgresql.org/docs/
- 29. Support ▪ Community based support – seems like a fast one too. ▪ Numerous companies specialized in enterprise support: http://www.postgresql.org/support/professional_support/ ▪ Enterprise database management companies like: EnterpriseDB ▪ Total Cost of Ownership is significantly lower even with enterprise support. (Based on reports. e.g. Gartner 2015).
- 30. vs. MySQL ▪ ACID fully! compliant. ▪ Subqueries and Joins. ▪ Better locking mechanism. ▪ JSONJSONB support. ▪ NoSQL and Key-Value store. ▪ Advanced GIS abilities. ▪ Full Text Search abilities. ▪ Advanced and attractive data types. ▪ Way better and useful extensibility patterns. ▪ Licensing issues.
- 31. vs. PostgreSQL ▪ Partitioning based on table inheritance (Pros. and Cons.) ▪ Can be an overkill in case of simple read- heavy operations. (Improved in newer versions). ▪ Replication and Clustering (especially multi-master). Not “there” yet, but on a right track. ▪ Popularity – not as popular as MySQL (for example) but gains popularity constantly, as opposite to MySQL. ▪ Expertise issues – different syntax and administration (compared to MSSQL).
- 32. THANK YOU