| name | spark-3.5-updates |
| description | Knowledge base for Spark 3.1→3.5 updates not covered by the Learning Spark 2nd Ed book. Use for: (A) Pandas UDF and Arrow APIs (exam Sec 7 obj 2 'Create and invoke Pandas UDF'); (B) new built-in functions added in 3.3-3.5 (exam Sec 3); (C) AQE evolution post-3.0 — default-on, new sub-configs, SMJ→SHJ conversion (exam Sec 4). NOT covered here: Pandas API on Spark (see skill pandas-on-spark), Spark Connect (see skill spark-connect), AQE 3.0 baseline (see apache-spark ch07/ch12). |
| allowed-tools | ["Read","Grep"] |
| argument-hint | ["topic A/B/C","function name","or chapter"] |
Spark 3.1 → 3.5 Updates — Exam-Prep Knowledge Base
Anchor: Spark 3.5 | Chapters: 3 | Generated: 2026-05-24
Scope: only what changed BETWEEN the Damji book (Spark 3.0 baseline) AND Spark 3.5. Anything Spark 4.x is flagged ⚠️.
Topics covered:
- A: Pandas UDF type hints + Pandas Function APIs + Arrow Python UDF — Sec 7 obj 2
- B: New built-in functions added in Spark 3.3-3.5 — Sec 3 supplement
- C: AQE evolution vs Spark 3.0 baseline — Sec 4 supplement
Cross-references (don't expand here):
- Pandas API on Spark (
pyspark.pandas) → skill pandas-on-spark
- Spark Connect → skill
spark-connect
- AQE 3.0 baseline + Spark UI →
apache-spark/chapters/ch07-tuning.md
- Spark 3.0 epilogue / Catalyst →
apache-spark/chapters/ch12-epilogue-spark-3.md
How to Use This Skill
- Without arguments — load the Core Frameworks below.
- By topic letter — A (Pandas UDF), B (functions), C (AQE).
- By chapter —
ch01, ch02, ch03.
Core Frameworks & Mental Models
Topic A — Pandas UDF (Sec 7 obj 2)
Pandas UDF = vectorized Python UDF using Arrow as transport between JVM and Python. Spark 3.0+ uses Python type hints; legacy PandasUDFType (book Damji ch.5) is deprecated.
The 4 type-hint shapes
| # | Type hint | Use |
|---|
| 1 | pd.Series, ... -> pd.Series | Series-to-Series scalar |
| 2 | Iterator[pd.Series] -> Iterator[pd.Series] | Iterator scalar (per-worker state) |
| 3 | Iterator[Tuple[pd.Series, ...]] -> Iterator[pd.Series] | Iterator multi-Series |
| 4 | pd.Series, ... -> Any | Grouped aggregate / window |
Pandas Function APIs (whole DataFrame, not column)
df.mapInPandas(func, schema) — iterator pd.DataFrame → iterator pd.DataFramedf.groupby(k).applyInPandas(func, schema) — one pd.DataFrame per groupdf.groupby(k).cogroup(other.groupby(k)).applyInPandas(func, schema) — two pd.DataFrames per cogroup
Arrow Python UDF (Spark 3.5+)
@udf(returnType="int", useArrow=True)
def f(s): return len(s)
useArrow=True/False/None. None → fall back to spark.sql.execution.pythonUDF.arrow.enabled- Better type coercion than default pickled UDF; still row-by-row (not vectorized)
Legacy → modern (Damji book mapping)
| Legacy | Modern |
|---|
PandasUDFType.SCALAR | type hint #1 |
PandasUDFType.SCALAR_ITER | type hint #2 |
PandasUDFType.GROUPED_AGG | type hint #4 |
PandasUDFType.GROUPED_MAP | groupby().applyInPandas (not @pandas_udf) |
Topic B — New functions 3.3 → 3.5 (Sec 3)
Filtered, exam-curated set (full table in cheatsheet.md and ch02). Highlights:
- Array (3.4+):
array_append, array_insert, array_compact, array_sort(comparator), array_prepend (3.5)
- Array (3.3):
array_size, try_element_at
- String (3.3):
contains, startswith, endswith, ilike, to_number
- String (3.4):
split_part, regexp_count, regexp_substr, mask
- Date/time (3.3):
timestampadd, timestampdiff, convert_timezone, make_date
- Aggregate (3.4):
median, mode, any_value, percentile_cont, percentile_disc
- Aggregate (3.3):
max_by, min_by, array_agg
- Misc:
equal_null (3.4), unpivot/melt (3.4), DataFrame.offset (3.5), assertDataFrameEqual (3.5)
Topic C — AQE delta vs 3.0 (Sec 4)
Read apache-spark/chapters/ch07-tuning.md and ch12-epilogue-spark-3.md first for the 3.0 baseline.
Big-picture delta
| Spark 3.0 (book) | Spark 3.5 |
|---|
spark.sql.adaptive.enabled default | false (opt-in) | true (since 3.2 — SPARK-33679) |
| Runtime join switches | SMJ → BHJ | + SMJ → ShuffledHashJoin (3.2) |
| DPP × AQE | separate | fully compatible (3.2) |
Key new configs (exam-worthy)
spark.sql.adaptive.enabled — true by default since 3.2…coalescePartitions.parallelismFirst — true by default (3.2); ⚠ silently ignores advisoryPartitionSizeInBytes…coalescePartitions.minPartitionSize — 1 MB default (3.2)…maxShuffledHashJoinLocalMapThreshold — 0 default; >0 enables SMJ→SHJ (3.2)…autoBroadcastJoinThreshold (AQE-specific) — overrides static for runtime decisions (3.2)…forceOptimizeSkewedJoin — false default; force skew handling even with extra shuffle (3.3)…rebalancePartitionsSmallPartitionFactor — 0.2 default; merge tiny fragments after skew splitting (3.3)…optimizer.excludedRules — disable AQE rules by name (3.1)…customCostEvaluatorClass — pluggable cost evaluator (3.2)
Top exam pitfalls
parallelismFirst=true (3.2 default) ignores advisoryPartitionSizeInBytes.- AQE on by default since 3.2 means upgrade silently enables it.
- SMJ → SHJ is a new join switch (3.2), distinct from SMJ → BHJ (3.0).
Exam Sec coverage mapping
| Topic | Exam section | Status |
|---|
| Pandas UDF + Function APIs + Arrow UDF | Sec 7 obj 2 | ✅ this skill |
| New built-in functions 3.3-3.5 | Sec 3 supplement | ✅ this skill |
| AQE evolution | Sec 4 supplement | ✅ this skill |
| AQE 3.0 baseline | Sec 4 | → apache-spark ch07/ch12 |
| Pandas API on Spark | Sec 7 obj 1 | → skill pandas-on-spark |
| Spark Connect | Sec 6 | → skill spark-connect |
| All other DataFrame/SQL/Streaming/Architecture | Sec 1-5 | → skill apache-spark |
Chapter Index
| # | Title | Topic | Focus |
|---|
| ch01 | Pandas UDF & Arrow APIs | A | 4 type hints, Pandas Function APIs, Arrow Python UDF, legacy mapping |
| ch02 | New Built-in Functions 3.3-3.5 | B | ~25 functions, version-tagged, with purpose |
| ch03 | AQE Delta vs Spark 3.0 | C | New configs, SMJ→SHJ, parallelismFirst gotcha |
Topic Index
Topic A (Pandas UDF / Arrow)
@pandas_udf → ch01@udf(useArrow=True) (Spark 3.5+) → ch01- 4 type-hint shapes → ch01
applyInPandas / mapInPandas / cogroup().applyInPandas → ch01PandasUDFType (legacy) → ch01- Arrow batch size config → ch01
selfDestruct (3.2+) → ch01
Topic B (new functions)
- Array:
array_append/insert/compact/sort/prepend/size, try_element_at, array_agg → ch02
- String:
contains/startswith/endswith/ilike, to_number, split_part, regexp_*, mask → ch02
- Date/time:
timestampadd/diff, convert_timezone, make_date → ch02
- Aggregate:
median, mode, any_value, percentile_cont/disc, max_by/min_by, histogram_numeric → ch02
- Misc:
equal_null, unpivot/melt, DataFrame.offset (3.5), assertDataFrameEqual (3.5), map_contains_key → ch02
Topic C (AQE)
spark.sql.adaptive.enabled default change → ch03parallelismFirst gotcha → ch03forceOptimizeSkewedJoin → ch03maxShuffledHashJoinLocalMapThreshold → ch03- SMJ → SHJ runtime conversion → ch03
- AQE × DPP compatibility → ch03
excludedRules / customCostEvaluatorClass → ch03- Local Shuffle Reader → ch03
Supporting Files
Sources used
Topic A:
Topic B:
Topic C:
To regenerate local snapshots: fetch each URL above and extract the relevant sections (the chapter files in chapters/ synthesize them).
Scope & Limits
This skill is calibrated for the exam objectives not covered by the Damji book due to its 2020 publication anchored on Spark 3.0. For everything book-covered, see apache-spark. For Pandas API on Spark or Spark Connect, see the dedicated skills.
