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diff --git a/content/posts/analytics/index.md b/content/posts/analytics/index.md new file mode 100644 index 0000000..e098b3a --- /dev/null +++ b/content/posts/analytics/index.md @@ -0,0 +1,304 @@ +--- +title: "Managing analytical workloads in startups" +draft: true +description: "" +categories: + - PostgreSQL +--- + +## Current issues + +- Issues with querying large dataset in online postgres (90GB) +- The dataset is analytics events and some business models. +- Often used for AB testing purpose, for instance measuring conversion rates grouped by some experiment values. +- We a have a readonly database, the queries are slow + +## Hypothesis/Solutions + +- Writing better queries: this also needs to be maintainable (e.g. we add more and more queries, it should not need to be carefully handcrafted) +- Using another engine + - Postgres like but OLAP oriented + - Pure OLAP (Clockhouse, Duckdb) + - AWS for simplicity (Athena or Redshift Spectrum) + - Scripts on parquet files: maximum adaptability +- How to get the data? + - it seems that it is already parquets + - it looks too slow to download all the data periodically: process in the cloud or incremental sync +- Parquet: + - possible to cluster by event? by datetime? + - sounds like recreating bigquery + +## Exporting cloud data + +- In AWS RDS snapshot, we can export only part of the data. In our case, we can start by `greengobackend.public.core_experimentationanalyticsevent` +- The whole database size is supposed to be 390GB, with 65GB of `core_experimentationanalyticsevent` +- The exported folder in S3 is only 3.8GB of parquet files, impressive +- There are 20M events in the database + +## DuckDB + +- We may try later to query from s3, for now simply download it +- `aws s3 sync s3://bi-data-test-gg/export-experimentationanalyticsevent experimentationanalyticsevent` +- As always when I use this type of tool, it ate my RAM and my desktop crashed +- It looks like it's not the easiest on my machine to create table from parquet files + Let's try a simple summary request to compare potential performances + +```sql +with growthbook_experimentationanalyticsevent as ( +select + sent_at AS timestamp, + device_information ->> 'greengo_device_id' AS device_id, + event ->> 'customEventID' AS custom_event_id +from 'experimentationanalyticsevent/greengobackend/public.core_experimentationanalyticsevent/1/*.gz.parquet' +) +select custom_event_id, count(*) from growthbook_experimentationanalyticsevent group by custom_event_id; +``` + +I will then run the same query on my Big production postgres, and also on my readonly replica (smaller). It doesn't use more than 600MB on duckdb. + +| Context | Duration | +| ------------------- | -------- | +| Duckdb | 10.1s | +| Duckdb 16GB | 10.0s | +| Postgres production | 180s | +| Postgres readonly | | + +## Analytical workload + +Then I'd like to run a real workload : an AB test experiment result. +It requires only a few language tweaks and type cast. +The result is slower than I expected (49.5s), but the same query on production did not complete in several minutes. + +The query ran directly on the parquet file: let's try to create a physical backed table within duckdb to see if it improves overall latency. `CREATE TABLE growthbook_experimentationanalyticsevent AS SELECT * FROM 'file.gz.parquet'` +I get some conversion error while trying to create a table from the query. +Creating the table takes more than 20GB of RAM, not easy on my system. +The table is 50GB large. I expect queries to run faster du to less compression. + +> The memory_limit setting controls how much data DuckDB is allowed to keep in memory. By default, this is set to 80% of the physical RAM of your system (e.g., if your system has 16 GB RAM, this defaults to 12.8 GB). The memory limit can be changed using the following command: `SET memory_limit = '4GB';` + +Weirdly, it is slower when reading from the materialized table. + +We can try to go once step further, and materializing the CTE in a parquet file. `COPY (SELECT * FROM 'file.gz.parquet') TO 'file-2.gz.parquet' (FORMAT parquet)`. Desktop died once again, I will now limit memory to 16GB (and run again some of the benchmarks). + +I don't understand, I can select the CTE, but when I materialize it I have a format error. I'll use [TRY_CAST](https://duckdb.org/docs/stable/sql/expressions/cast.html#try_cast) instead of CAST. + +First I exported the CTE to a compressed parquet: it took >2m, it's 3.0GB large. Let's try to perform the query on it. + +Then export the CTE to a physical duckdb: the previous db size was 50GB, it took 2m42, the database is now 82GB, so I expect 32GB of new data. Let's perform the query on it. + +The results are shocking, I don't even have indices (ok maybe yuo don't do indices on OLAP databases). + +Some insights taken from explain analyze: + +- Table scan (20M rows) filtered into 14k rows takes 600ms (ok numbers don't add up) +- The same parquet scan (14k of 20M) takes 940ms. It's very fast. +- The multiple parquet scan take 80s, it's a bottleneck. The filtering seems to be done later. + +| Context | Duration | +| -------------------------------------- | -------- | +| CTE from parquet | 49.5s | +| CTE from physical table | 1m22 | +| CTE materialized in compressed parquet | 600ms | +| CTE materialized in physical table | 300ms | + +The expected results for reference: + +``` +variation dimension users main_sum main_sum_squares covariate_sum covariate_sum_squares main_covariate_sum_product +0 "" 4647 603 603.0 10 10.0 3 +__multiple__ "" 1 0 0.0 0 0.0 0 +1 "" 4422 589 589.0 12 12.0 4 +``` + +{{<details summary="Our sample analytics query for AB test experiment">}} + +```sql +WITH growthbook_experimentationanalyticsevent AS ( + SELECT + CAST(sent_at) AS timestamp, + device_information ->> 'greengo_device_id' AS device_id, + event ->> 'customEventID' AS custom_event_id, + event ->> 'elementLabel' AS element_label, + event ->> 'elementSecondaryLabel' AS element_secondary_label, + -- Strings + event -> 'value' -> 'experiment' ->> 'key' AS experiment_id, + event ->> 'featureKey' AS feature_key, + event ->> 'step' AS step, + event ->> 'filterItem' AS filter_item, + event ->> 'paymentType' AS payment_type, + event -> 'tripSearchFormValues' ->> 'tripSearchMode' AS trip_search_mode_from_form_values, + event -> 'hostingSearchParameters' ->> 'tripSearchMode' AS trip_search_mode_from_hosting_search_parameters, + event ->> 'bookingMode' AS booking_mode, + -- JSON + event -> 'featureValue' AS feature_value, + event -> 'value' AS _value, + -- Cast + CAST(event -> 'value' -> 'result' ->> 'variationId' AS int) AS variation_id, + CAST(event ->> 'index' AS int) AS _index, + CAST(event ->> 'isSelected' AS boolean) AS is_selected, + CAST(event ->> 'isScrollable' AS boolean) AS is_scrollable, + CAST(event ->> 'numberOfFullyVisibleFilters' AS int) AS number_of_fully_visible_filters, + CAST(event ->> 'value' AS varchar) AS value_as_string, + CAST(event ->> 'totalPrice' AS decimal) AS total_price, + CAST(event ->> 'totalPricePaidAsMoney' AS decimal) AS total_price_paid_as_money + FROM + core_experimentationanalyticsevent +), +__rawExperiment AS ( + SELECT + device_id, + timestamp, + experiment_id, + variation_id + FROM + growthbook_experimentationanalyticsevent + WHERE + custom_event_id = 'CE0158' + AND timestamp BETWEEN '2025-03-18 18:00:00' AND '2025-04-11 07:01:02' +), +__experimentExposures AS ( + -- Viewed Experiment + SELECT + e.device_id AS device_id, + CAST(e.variation_id AS varchar) AS variation, + e.timestamp AS timestamp + FROM + __rawExperiment e + WHERE + e.experiment_id = 'new-checkout-mobile-conversion-rate' + AND e.timestamp >= '2025-03-18 18:00:00' + AND e.timestamp <= '2025-04-11 07:01:02' +), +__experimentUnits AS ( + -- One row per user + SELECT + e.device_id AS device_id, +( + CASE WHEN COUNT(DISTINCT e.variation) > 1 THEN + '__multiple__' + ELSE + MAX(e.variation) + END) AS variation, + MIN(e.timestamp) AS first_exposure_timestamp + FROM + __experimentExposures e + GROUP BY + e.device_id +), +__distinctUsers AS ( + SELECT + device_id, + CAST('' AS varchar) AS dimension, + variation, + first_exposure_timestamp AS timestamp, + DATE_TRUNC('day', first_exposure_timestamp) AS first_exposure_date, + first_exposure_timestamp AS preexposure_end, + first_exposure_timestamp - INTERVAL '336 hours' AS preexposure_start + FROM + __experimentUnits +), +__metric AS ( + -- Metric ([LEGACY] Booking Purchase) + SELECT + device_id AS device_id, + 1 AS value, + m.timestamp AS timestamp + FROM ( + SELECT + timestamp, + device_id, + custom_event_id, + element_label, + element_secondary_label, + feature_key, + booking_mode, + step, + _index, + is_selected, + filter_item, + payment_type, + is_scrollable, + number_of_fully_visible_filters, + _value, + value_as_string, + trip_search_mode_from_form_values, + trip_search_mode_from_hosting_search_parameters, + total_price + FROM + growthbook_experimentationanalyticsevent + WHERE + timestamp BETWEEN '2025-03-04 18:00:00' AND '2025-04-11 07:01:02') m + WHERE + custom_event_id = 'CE0026' + AND m.timestamp >= '2025-03-04 18:00:00' + AND m.timestamp <= '2025-04-11 07:01:02' +), +__userMetricJoin AS ( + SELECT + d.variation AS variation, + d.dimension AS dimension, + d.device_id AS device_id, +( + CASE WHEN m.timestamp >= d.timestamp + AND m.timestamp <= '2025-04-11 07:01:02' THEN + m.value + ELSE + NULL + END) AS value +FROM + __distinctUsers d + LEFT JOIN __metric m ON (m.device_id = d.device_id) +), +__userMetricAgg AS ( + -- Add in the aggregate metric value for each user + SELECT + umj.variation AS variation, + umj.dimension AS dimension, + umj.device_id, + COALESCE(MAX(umj.value), 0) AS value + FROM + __userMetricJoin umj + GROUP BY + umj.variation, + umj.dimension, + umj.device_id +), +__userCovariateMetric AS ( + SELECT + d.variation AS variation, + d.dimension AS dimension, + d.device_id AS device_id, + COALESCE(MAX(value), 0) AS value + FROM + __distinctUsers d + JOIN __metric m ON (m.device_id = d.device_id) + WHERE + m.timestamp >= d.preexposure_start + AND m.timestamp < d.preexposure_end + GROUP BY + d.variation, + d.dimension, + d.device_id) + -- One row per variation/dimension with aggregations + SELECT + m.variation AS variation, + m.dimension AS dimension, + COUNT(*) AS users, + SUM(COALESCE(m.value, 0)) AS main_sum, + SUM(POWER(COALESCE(m.value, 0), 2)) AS main_sum_squares, + SUM(COALESCE(c.value, 0)) AS covariate_sum, + SUM(POWER(COALESCE(c.value, 0), 2)) AS covariate_sum_squares, + SUM(COALESCE(m.value, 0) * COALESCE(c.value, 0)) AS main_covariate_sum_product +FROM + __userMetricAgg m + LEFT JOIN __userCovariateMetric c ON (c.device_id = m.device_id) +GROUP BY + m.variation, + m.dimension +``` + +{{</details>}} + +## Resources + +- https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_ExportSnapshot.html |