I have a large SQLite database (~134 GB) that has multiple tables each with 14 columns, about 330 million records, and 4 indexes. The only operation used on the database is "Select *" as I need all the columns(No inserts or updates). When I query the database, the response time is slow when the result set is big (takes 160 seconds for getting ~18,000 records).
I have improved the use of indexes multiple times and this is the fastest response time I got.
I am running the database as a back-end database for a web application on a server with 32 GB of RAM.
is there a way to use RAM (or anything else) to speed up the query process?
Here is the code that performs the query.
async.each(proteins,function(item, callback) {
`PI[item] = []; // Stores interaction proteins for all query proteins
PS[item] = []; // Stores scores for all interaction proteins
PIS[item] = []; // Stores interaction sites for all interaction proteins
var sites = {}; // a temporarily holder for interaction sites
var query_string = 'SELECT * FROM ' + organism + PIPE_output_table +
' WHERE ' + score_type + ' > ' + cutoff['range'] + ' AND (protein_A = "' + item + '" OR protein_B = "' + item '") ORDER BY PIPE_score DESC';
db.each(query_string, function (err, row) {
if (row.protein_A == item) {
PI[item].push(row.protein_B);
// add 1 to interaction sites to represent sites starting from 1 not from 0
sites['S1AS'] = row.site1_A_start + 1;
sites['S1AE'] = row.site1_A_end + 1;
sites['S1BS'] = row.site1_B_start + 1;
sites['S1BE'] = row.site1_B_end + 1;
sites['S2AS'] = row.site2_A_start + 1;
sites['S2AE'] = row.site2_A_end + 1;
sites['S2BS'] = row.site2_B_start + 1;
sites['S2BE'] = row.site2_B_end + 1;
sites['S3AS'] = row.site3_A_start + 1;
sites['S3AE'] = row.site3_A_end + 1;
sites['S3BS'] = row.site3_B_start + 1;
sites['S3BE'] = row.site3_B_end + 1;
PIS[item].push(sites);
sites = {};
}
}