Groonga 10.0.5 has been released
Groonga 10.0.5 has been released!
How to install: Install
Changes
Here are important changes in this release:
-
select Added support for storing reference in table that we specify with
--load_table. -
select Improved sort performance.
-
select Improved performance a bit on below cases.
- A case of searching for many records matches.
- A case of drilldown for many records.
-
[aggregator] Added support for score accessor for target.
-
indexing Improved performance of offline index construction on VC++ version.
-
select Use
nullinsteadNaN,Infinity, and-Infinitywhen Groonga outputs result for JSON format.- Because JSON doesn't support them.
-
select Add support fot aggregating standard deviation value.
-
[Windows] Dropped Visual Studio 2013 support.
-
Groonga HTTP Server Fixed a bug that a request can't halt even if we execute
shutdown?mode=immediatewhen the response was halted by error occurrence. -
Fixed a crash bug when an error occurs while a request.
- It only occurs when we use Apache Arrow Format.
- Groonga crashes when we send request to Groonga again after the previous request was halted by error occurrence.
-
between Fixed a crash bug when temporary table is used.
- For example, if we specify a dynamic column in the first argument for
between, Groonga had crashed.
- For example, if we specify a dynamic column in the first argument for
-
Fixed a bug that procedure created by plugin is freed unexpectedly.
- It's only occurred in reference count mode.
- It's not occurred if we don't use
plugin_register. - It's not occurred in the process that executes
plugin_register. - It's occurred in the process that doesn't execute
plugin_register.
-
Fixed a bug that normalization error occurred while static index construction by
token_column.
select Added support for storing reference in table that we specify with --load_table.
-
--load_tableis a feature that stores search results to the table in a prepared.- If the searches are executed multiple times, we can cache the search results by storing them to this table.
- We can shorten the search times that the search after the first time by using this table.
-
We can store a reference to other tables into the key of this table as below since this release.
- We can make a smaller size of this table. Because we only store references without store column values.
-
If we search against this table, we can search by using indexes for reference destination.
table_create Logs TABLE_HASH_KEY ShortText column_create Logs timestamp COLUMN_SCALAR Time table_create Times TABLE_PAT_KEY Time column_create Times logs_timestamp COLUMN_INDEX Logs timestamp table_create LoadedLogs TABLE_HASH_KEY Logs load --table Logs [ { "_key": "2015-02-03:1", "timestamp": "2015-02-03 10:49:00" }, { "_key": "2015-02-03:2", "timestamp": "2015-02-03 12:49:00" }, { "_key": "2015-02-04:1", "timestamp": "2015-02-04 00:00:00" } ] select \ Logs \ --load_table LoadedLogs \ --load_columns "_key" \ --load_values "_key" \ --limit 0 select \ --table LoadedLogs \ --filter 'timestamp >= "2015-02-03 12:49:00"' [ [ 0, 0.0, 0.0 ], [ [ [ 2 ], [ [ "_id", "UInt32" ], [ "_key", "ShortText" ], [ "timestamp", "Time" ] ], [ 2, "2015-02-03:2", 1422935340.0 ], [ 3, "2015-02-04:1", 1422975600.0 ] ] ] ]
select Improved sort performance on below cases.
-
When many sort keys need ID resolution.
-
For example, the following expression needs ID resolution.
--filter true --sort_keys column
-
For example, the following expression doesn't need ID resolution. Because the
_scorepseudo column exists in the result table not the source table.--filter true --sort_keys _score
-
-
When a sort target table has a key.
- Therefore,
TABLE_NO_KEYisn't supported this improvement.
- Therefore,
[aggregator] Added support for score accessor for target.
-
For example, we can
_scoresubject toaggregator_*as below.table_create Items TABLE_HASH_KEY ShortText column_create Items price COLUMN_SCALAR UInt32 column_create Items tag COLUMN_SCALAR ShortText load --table Items [ {"_key": "Book", "price": 1000, "tag": "A"}, {"_key": "Note", "price": 1000, "tag": "B"}, {"_key": "Box", "price": 500, "tag": "B"}, {"_key": "Pen", "price": 500, "tag": "A"}, {"_key": "Food", "price": 500, "tag": "C"}, {"_key": "Drink", "price": 300, "tag": "B"} ] select Items \ --filter true \ --drilldowns[tag].keys tag \ --drilldowns[tag].output_columns _key,_nsubrecs,score_mean \ --drilldowns[tag].columns[score_mean].stage group \ --drilldowns[tag].columns[score_mean].type Float \ --drilldowns[tag].columns[score_mean].flags COLUMN_SCALAR \ --drilldowns[tag].columns[score_mean].value 'aggregator_mean(_score)' [ [ 0, 0.0, 0.0 ], [ [ [ 6 ], [ [ "_id", "UInt32" ], [ "_key", "ShortText" ], [ "price", "UInt32" ], [ "tag", "ShortText" ] ], [ 1, "Book", 1000, "A" ], [ 2, "Note", 1000, "B" ], [ 3, "Box", 500, "B" ], [ 4, "Pen", 500, "A" ], [ 5, "Food", 500, "C" ], [ 6, "Drink", 300, "B" ] ], { "tag": [ [ 3 ], [ [ "_key", "ShortText" ], [ "_nsubrecs", "Int32" ], [ "score_mean", "Float" ] ], [ "A", 2, 1.0 ], [ "B", 3, 1.0 ], [ "C", 1, 1.0 ] ] } ] ]
select Add support fot aggregating standard deviation value.
-
For example, we can calculate a standard deviation for every group as below.
table_create Items TABLE_HASH_KEY ShortText column_create Items price COLUMN_SCALAR UInt32 column_create Items tag COLUMN_SCALAR ShortText load --table Items [ {"_key": "Book", "price": 1000, "tag": "A"}, {"_key": "Note", "price": 1000, "tag": "B"}, {"_key": "Box", "price": 500, "tag": "B"}, {"_key": "Pen", "price": 500, "tag": "A"}, {"_key": "Food", "price": 500, "tag": "C"}, {"_key": "Drink", "price": 300, "tag": "B"} ] select Items \ --drilldowns[tag].keys tag \ --drilldowns[tag].output_columns _key,_nsubrecs,price_sd \ --drilldowns[tag].columns[price_sd].stage group \ --drilldowns[tag].columns[price_sd].type Float \ --drilldowns[tag].columns[price_sd].flags COLUMN_SCALAR \ --drilldowns[tag].columns[price_sd].value 'aggregator_sd(price)' \ --output_pretty yes [ [ 0, 1594339851.924836, 0.002813816070556641 ], [ [ [ 6 ], [ [ "_id", "UInt32" ], [ "_key", "ShortText" ], [ "price", "UInt32" ], [ "tag", "ShortText" ] ], [ 1, "Book", 1000, "A" ], [ 2, "Note", 1000, "B" ], [ 3, "Box", 500, "B" ], [ 4, "Pen", 500, "A" ], [ 5, "Food", 500, "C" ], [ 6, "Drink", 300, "B" ] ], { "tag": [ [ 3 ], [ [ "_key", "ShortText" ], [ "_nsubrecs", "Int32" ], [ "price_sd", "Float" ] ], [ "A", 2, 250.0 ], [ "B", 3, 294.3920288775949 ], [ "C", 1, 0.0 ] ] } ] ]- We can also calculate sample standard deviation to specifing
aggregate_sd(target, {"unbiased": true}).
- We can also calculate sample standard deviation to specifing
Conclusion
Let's search by Groonga!