How to serve a searchable database of more than a million records

1. How do I search a million records?
2. How do I manage millions of records in SQL Server?
3. Can MongoDB handle millions of data?
4. Can MySQL handle millions of records?
5. What is the fastest way to search for millions of records in SQL Server?
6. How can I make my database search faster?
7. How much data can Postgres handle?
8. How many rows is too many for Postgres?
9. How do you optimize a DB when there are millions of records constantly updating?
10. How many documents can MongoDB handle?
11. Which database is best for large data?
12. Is MongoDB good for reporting?

How do I search a million records?

1. retrieve millions record from SQL server in seconds.
2. Make sql query vary vary fast.
3. sql select query to select distinct records.
4. Optimized select query to get 10 by 10 records form millions of records.
5. Sql Query to select last Row of The Table.
6. Need help to Update Millions of record in Oracle Table...

How do I manage millions of records in SQL Server?

Use the SQL Server BCP to export big tables data

In this example, we will export the SalesPerson table data into a text file. This table includes 100 million rows and it's size is about 7.5 GB. In our first testing, we will run the SQL Server BCP with default values in order to export 100 M rows.

Can MongoDB handle millions of data?

MongoDB - Querying performance for over 10 million records.

Can MySQL handle millions of records?

MySQL can easily handle many millions of rows, and fairly large rows at that.

What is the fastest way to search for millions of records in SQL Server?

When you load new data, check if any of the domain names are new - and insert those into the Domains table. Then in your big table, you just include the DomainID. Not only will this keep your 50 million row table much smaller, it will also make lookups like this much more efficient.

How can I make my database search faster?

Try these five tips to boost the speed of your database:

1. Make sure all of your tables have primary keys. Running a table without a primary key is like running a four-cylinder engine with only two active pistons. ...
2. Optimize by adding secondary indexes. ...
3. Be like an atom and split. ...
4. Use Compact and Repair. ...
5. Load only what you need.

How much data can Postgres handle?

PostgreSQL does not impose a limit on the total size of a database. Databases of 4 terabytes (TB) are reported to exist. A database of this size is more than sufficient for all but the most demanding applications.

How many rows is too many for Postgres?

Q: What is the PostgreSQL database top capacity?

How do you optimize a DB when there are millions of records constantly updating?

make sure the database in in bulk-logged or simple recovery model.
...
For a table w/out a clustered index, during a time w/out concurrent DML:

1. SELECT *, new_col = 1 INTO clone. BaseTable FROM dbo. BaseTable.
2. recreate indexes, constraints, etc on new table.
3. switch old and new w/ ALTER SCHEMA ... TRANSFER.
4. drop old table.

How many documents can MongoDB handle?

If you specify a maximum number of documents for a capped collection using the max parameter to create , the limit must be less than 2 ³² documents. If you do not specify a maximum number of documents when creating a capped collection, there is no limit on the number of documents.

Which database is best for large data?

TOP 10 Open Source Big Data Databases

• Cassandra. Originally developed by Facebook, this NoSQL database is now managed by the Apache Foundation. ...
• HBase. Another Apache project, HBase is the non-relational data store for Hadoop. ...
• MongoDB. MongoDB was designed to support humongous databases. ...
• Neo4j. ...
• CouchDB. ...
• OrientDB. ...
• Terrstore. ...
• FlockDB.

Is MongoDB good for reporting?

NoSQL databases like MongoDB offer superior benefits when dealing with big data over SQL because of their flexible schema requirements. ... Nonetheless, people often need to do analysis on MongoDB data. Historically, it has been common to replicate MongoDB data into a SQL database for analytics using a process like ETL.