I'm wondering if I can make use of the information provided by the Query Report and Query Plan tabs on the portal catalog. Can I optimize ZCatalog queries based on the query report? How does ZCatalogs Query Plan differ from a query plan of an SQL database?
The query plan information is used to improve catalog performance, but you cannot optimize your own queries based on plan information.
The catalog only builds up that information as needed, based on your index sizes; unlike a SQL database the catalog does not plan each query based on such information but rather looks up pre-calculated plans from the structure reflected in the Query Plan tab.
The query report tab does give you information about what indexes are performing poorly for your code; you may want to rethink code that uses those combinations of indexes and/or look into why those indexes performed poorly; perhaps your query didn't limit the result quickly enough or the slow index is very large, indicating that perhaps your ZODB cache is too small to hold that large index or that other results keep pushing it out.
On the whole, for large applications it is a good idea to retain the query plan; in one project we dump cache information before stopping instances and reload that after starting again, and that includes the catalog query plan:
plan = site.portal_catalog.getCatalogPlan()
with open(PLAN_PATH, 'w') as out:
out.write(plan)
and on load:
if os.path.exists(PLAN_PATH):
from Products.ZCatalog.plan import PriorityMap
try:
PriorityMap.load_from_path(PLAN_PATH)
except Exception:
pass
Related
I am exploring using ADX as a timeseries data store for sensor metrics. Our current solution is storing data in MSSQL and I'm testing ADX as an alternative. I was able to set up data ingestion and I can perform basic queries, and with the added aggregation functions, computing insights and statistics seems to be much faster.
As part of the solution, we have a API data access layer used by clients and our web portal to query data for display and analysis use. I am currently transforming the MSSQL queries to the KQL version and I'm hitting a stumble block on data pagination.
We have a function to query historical data using a combination of:
an start/end date,
a device identifier,
and some paging options
records per page,
current page,
column sorting / additional filtering
Currently this is handled in a SQL SP on the back-end, by getting the total number of records and pages (which is set as output on the API so that the front-end can use this data in the table view), then getting the records based on the input parameters and pagination details to return a record set - quite straight forward.
Any suggestions on how to achieve effective pagination using ADX/KQL?
I found a section in the docs on pagination on stored query results, but as the queries are dynamic based on user input, so this does not sound like a viable option.
When you paginate (for example viewing result 21-30) you need to consider if you are taking a snapshot of the result and paging through it or viewing live data. If you expect new rows coming in to not affect your pagination, than stored query results is that snapshot. Once you generate it you can select specific rows from it based on your page calculation.
Let's assume that I know when a particular database record was updated. I know that somewhere exists a history of all SQL that's executed, perhaps only accessible by a DBA. If I could access this history, I could SELECT from it where the query text is LIKE '%fieldname%'. While this would pretty much pull up any transactional query containing the field name I am looking for, it's a great start, especially if I can filter the recordset down to a particular date/time range.
I've discovered the dbc.DBQLogTbl view, but it doesn't appear to work as I expect. Is there another view that contains the information I am looking for?
It depends on the level of database query logging (DBQL) that has been enabled by the DBA.
Some DBA's may elect not to detailed information for tactical queries so it is best to consult with your DBA team to understand what is being captured. You can also query the DBC.DBQLRules to determine what level of logging has been enabled.
The following data dictionary objects will be of particular interest to your question:
DBC.QryLog contains the details about the query with respect to the user, session, application, type of statement, CPU, IO, and other fields associated with a particular query.
DBC.QryLogSQL contains the SQL statements. If a SQL statement is exceeds a certain length it is split across multiple rows which is denoted by a column in this table. If you join this to the main Query Log table care must be taken if you are aggregating and metrics in the Query Log table. Although more often then not if your are joining the Query Log table to the SQL table you are not doing any aggregation.
DBC.QryLogObjects contains the objects used by a particular query and how they were used. This includes tables, columns, and indexes referenced by a particular query.
These tables can be joined together in DBC via QueryID and ProcID. There are a few other tables that capture information about the queries but are beyond the scope of this particular question. You can find out about those in the Teradata Manuals.
Check with your DBA team to determine the level of logging being done and where they historical DBQL data is retained. Often DBQL data is moved nightly to a historical database and there often is a ten minute delay in data being flushed from cache to the DBC tables. Your DBA team can tell you where to find historical DBQL data.
I have a reasonably complex query to extract the Id field of the results I am interested in based on parameters entered by the user.
After extracting the relevant Ids I am using the resulting set of Ids several times, in separate queries, to extract the actual output record sets I want (by joining to other tables, using aggregate functions, etc).
I would like to avoid running the initial query separately for every set of results I want to return. I imagine my situation is a common pattern so I am interested in what the best approach is.
The database is in MS SQL Server and I am using .NET 3.5.
It would definitely help if the question contained some measurements of the unoptimized solution (data sizes, timings). There is a variety of techniques that could be considered here, some listed in the other answers. I will assume that the reason why you do not want to run the same query repeatedly is performance.
If all the uses of the set of cached IDs consist of joins of the whole set to additional tables, the solution should definitely not involve caching the set of IDs outside of the database. Data should not travel there and back again if you can avoid it.
In some cases (when cursors or extremely complex SQL are not involved) it may be best (even if counterintuitive) to perform no caching and simply join the repetitive SQL to all desired queries. After all, each query needs to be traversed based on one of the joined tables and then the performance depends to a large degree on availability of indexes necessary to join and evaluate all the remaining information quickly.
The most intuitive approach to "caching" the set of IDs within the database is a temporary table (if named #something, it is private to the connection and therefore usable by parallel independent clients; or it can be named ##something and be global). If the table is going to have many records, indexes are necessary. For optimum performance, the index should be a clustered index (only one per table allowed), or be only created after constructing that set, where index creation is slightly faster.
Indexed views are cleary preferable to temporary tables except when the underlying data is read only during the whole process or when you can and want to ignore such updates to keep the whole set of reports consistent as far as the set goes. However, the ability of indexed views to always accurately project the underlying data comes at a cost of slowing down those updates.
One other answer to this question mentions stored procedures. This is largely a way of organizing your code. However, it if you go this way, it is preferable to avoid using temporary tables, because such references to a temporary table prevent pre-compilation of the stored procedure; go for views or indexed views if you can.
Regardless of the approach you choose, do not guess at the performance characteristics and query optimizer behavior. Learn to display query execution plans (within SQL Server Management Studio) and make sure that you see index accesses as opposed to nested loops combining multiple large sets of data; only add indexes that demonstrably and drastically change the performance of your queries. A well chosen index can often change the performance of a query by a factor of 1000, so this is somewhat complex to learn but crucial for success.
And last but not least, make sure you use UPDATE STATISTICS when repopulating the database (and nightly in production), or your query optimizer will not be able to put the indexes you have created to their best uses.
If you are planning to cache the result set in your application code, then ASP.NET has cache, Your Winform will have the object holding the data with it with which you can reuse the data.
If planning to do the same in SQL Server, you might consider using indexed views to find out the Id's. The view will be materialized and hence you can get the results faster. You might even consider using a staging table to hold the id's temporarily.
With SQL Server 2008 you can pass table variables as params to SQL. Just cache the IDs and then pass them as a table variable to the queries that fetch the data. The only caveat of this approach is that you have to predefine the table type as UDT.
http://msdn.microsoft.com/en-us/library/bb510489.aspx
For SQL Server, Microsoft generally recommends using stored procedures whenever practical.
Here are a few of the advantages:
http://blog.sqlauthority.com/2007/04/13/sql-server-stored-procedures-advantages-and-best-advantage/
* Execution plan retention and reuse
* Query auto-parameterization
* Encapsulation of business rules and policies
* Application modularization
* Sharing of application logic between applications
* Access to database objects that is both secure and uniform
* Consistent, safe data modification
* Network bandwidth conservation
* Support for automatic execution at system start-up
* Enhanced hardware and software capabilities
* Improved security
* Reduced development cost and increased reliability
* Centralized security, administration, and maintenance for common routines
It's also worth noting that, unlike other RDBMS vendors (like Oracle, for example), MSSQL automatically caches all execution plans:
http://msdn.microsoft.com/en-us/library/ms973918.aspx
However, for the last couple of versions of SQL Server, execution
plans are cached for all T-SQL batches, regardless of whether or not
they are in a stored procedure
The best approach depends on how often the Id changes, or how often you want to look it up again.
One technique is to simply store the result in the ASP.NET object cache, using the Cache object (also accessible from HttpRuntime.Cache). For example (from a page):
this.Cache["key"] = "value";
There are many possible variations on this theme.
You can use Memcached to cache values in the memory.
As I see there are some .net ports.
How frequently does the data change that you'll be querying? To me, this sounds like a perfect scenario for data warehousing, where you flatting the data for quicker data retrieval and create the tables exactly as your 'DTO' wants to see the data. This method is different than an indexed view in that it's simply a table which will have quick seek operations, and could especially be improved if you setup the indexes properly on the columns that you plan to query
You can create Global temporary Table. Create the table on the fly. Now insert the records as per your request. Access this table in your next request in your joins... for reusability
Background: I am using SQLite database in my flex application. Size of the database is 4 MB and have 5 tables which are
table 1 have 2500 records
table 2 have 8700 records
table 3 have 3000 records
table 4 have 5000 records
table 5 have 2000 records.
Problem: Whenever I run a select query on any table, it takes around (approx 50 seconds) to fetch data from database tables. This has made the application quite slow and unresponsive while it fetches the data from the table.
How can i improve the performance of the SQLite database so that the time taken to fetch the data from the tables is reduced?
Thanks
As I tell you in a comment, without knowing what structures your database consists of, and what queries you run against the data, there is nothing we can infer suggesting why your queries take much time.
However here is an interesting reading about indexes : Use the index, Luke!. It tells you what an index is, how you should design your indexes and what benefits you can harvest.
Also, if you can post the queries and the table schemas and cardinalities (not the contents) maybe it could help.
Are you using asynchronous or synchronous execution modes? The difference between them is that asynchronous execution runs in the background while your application continues to run. Your application will then have to listen for a dispatched event and then carry out any subsequent operations. In synchronous mode, however, the user will not be able to interact with the application until the database operation is complete since those operations run in the same execution sequence as the application. Synchronous mode is conceptually simpler to implement, but asynchronous mode will yield better usability.
The first time SQLStatement.execute() on a SQLStatement instance, the statement is prepared automatically before executing. Subsequent calls will execute faster as long as the SQLStatement.text property has not changed. Using the same SQLStatement instances is better than creating new instances again and again. If you need to change your queries, then consider using parameterized statements.
You can also use techniques such as deferring what data you need at runtime. If you only need a subset of data, pull that back first and then retrieve other data as necessary. This may depend on your application scope and what needs you have to fulfill though.
Specifying the database with the table names will prevent the runtime from checking each database to find a matching table if you have multiple databases. It also helps prevent the runtime will choose the wrong database if this isn't specified. Do SELECT email FROM main.users; instead of SELECT email FROM users; even if you only have one single database. (main is automatically assigned as the database name when you call SQLConnection.open.)
If you happen to be writing lots of changes to the database (multiple INSERT or UPDATE statements), then consider wrapping it in a transaction. Changes will made in memory by the runtime and then written to disk. If you don't use a transaction, each statement will result in multiple disk writes to the database file which can be slow and consume lots of time.
Try to avoid any schema changes. The table definition data is kept at the start of the database file. The runtime loads these definitions when the database connection is opened. Data added to tables is kept after the table definition data in the database file. If changes such as adding columns or tables, the new table definitions will be mixed in with table data in the database file. The effect of this is that the runtime will have to read the table definition data from different parts of the file rather than at the beginning. The SQLConnection.compact() method restructures the table definition data so it is at the the beginning of the file, but its downside is that this method can also consume much time and more so if the database file is large.
Lastly, as Benoit pointed out in his comment, consider improving your own SQL queries and table structure that you're using. It would be helpful to know your database structure and queries are the actual cause of the slow performance or not. My guess is that you're using synchronous execution. If you switch to asynchronous mode, you'll see better performance but that doesn't mean it has to stop there.
The Adobe Flex documentation online has more information on improving database performance and best practices working with local SQL databases.
You could try indexing some of the columns used in the WHERE clause of your SELECT statements. You might also try minimizing usage of the LIKE keyword.
If you are joining your tables together, you might try simplifying the table relationships.
Like others have said, it's hard to get specific without knowing more about your schema and the SQL you are using.
In my web application, I have a dynamic query that returns huge data to datatable, and this query is often recalled with different parameters. So database is exhausted.
I want to get all record with no parameters to an object, and perform queries (may be with linq) on this object. So database will not be exthausted.
Which objects can be used instead of datatable?
This is one of my pet peeves - people who return all the data from the database.
There is absolutely no need for this unless you are doing reporting.
If you are doing reporting, then you need to increase your hardware capability so that the database can cope. This may also include tuning your database, rearranging tables, reindexing, regular rebuilding of indexes, updating statistics, archiving out old data, etc.
If you are NOT doing reporting, then start limiting how much data can be queried at any one time. Users DO NOT need to see massive quantities of data all at once. They need to see discrete amounts of data presented in a manageable and coherent way.
Another rule of thumb i like to observe is: let your database server do the work, it is made to manipulate lots of data, it is what it is good at, and it should have the power to do it. Pulling back loads of data to the client, and then trying to manipulate that data on the client is a foolish thing to do. If your client machines are more powerful than the database server then you have issues.
Never ever perform this(except cache)!!!
You are trying to implement DB mechanisms, like
persistent storage
index search and query strategy
replication
and so on
Spend your time on db optimization(optimal scheme, indexes, query, partitioning).