IBM solidDB: In-Memory Database Optimized for Extreme
Speed and Availability
om, Vilho Raatikka, Jarmo Ruuth, Petri Soini, Katriina Vakkila
IBM Helsinki Lab, Oy IBM Finland Ab
email@example.com, raatikka@iki.ﬁ, Jarmo.Ruuth@ﬁ.ibm.com, Petri.Soini@ﬁ.ibm.com,
A relational in-memory database, IBM solidDB is used worldwide for its ability to deliver extreme
speed and availability. As the name implies, an in-memory database resides entirely in main memory
rather than on disk, making data access an order of several magnitudes faster than with conventional,
disk-based databases. Part of that leap is due to the fact that RAM simply provides faster data access
than hard disk drives. But solidDB also has data structures and access methods speciﬁcally designed
for storing, searching, and processing data in main memory. As a result, it outperforms ordinary disk-
based databases even when the latter have data fully cached in memory. Some databases deliver low
latency but cannot handle large numbers of transactions or concurrent sessions. IBM solidDB provides
throughput measured in the range of hundreds of thousands to million of transactions per second while
consistently achieving response times (or latency) measured in microseconds. This article explores the
structural differences between in-memory and disk-based databases, and how solidDB works to deliver
IBM solidDB [1, 18] is a relational database server that combines the high performance of in-memory tables
with the nearly unlimited capacity of disk-based tables. Pure in-memory databases are fast but strictly limited by
the size of memory. Pure disk-based databases allow nearly unlimited amounts of storage but their performance
is dominated by disk access. Even if the server has enough memory to store the entire database in memory,
database servers designed for disk-based tables can be slow because data structures that are optimal for disk-
based tables  are far from optimal for in-memory tables.
The solidDB solution is to provide a single hybrid database server  that contains two optimized engines:
•The disk-based engine (DBE) is optimized for disk-based access.
•The main-memory engine (MME) is optimized for in-memory access.
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Both engines coexist inside the same server process and a single SQL statement may access data from both
engines. The key components of solidDB MME technology (Vtrie and index concurrency control) are discussed
in more detail in section 2.
To take the full advantage of the in-memory technology, you can also link your client application directly
to the database server routines for higher performance and tighter control over the server. The shared memory
access (SMA) feature of solidDB uses direct function calls to the solidDB server code. The in-memory database
is located in a shared memory segment available to all applications connected to the server via SMA. Multi-
ple concurrent applications running in separate processes can utilize this connection option to reduce response
times signiﬁcantly. This means that the applications ODBC or JDBC requests are processed almost fully in the
application process space, with no need for a context switch among processes.
In addition to a fully functional relational database server, solidDB provides synchronization features be-
tween multiple solidDB instances or between solidDB and other enterprise data servers. The solidDB server
can also be conﬁgured for high availability. Synchronization between solidDB instances uses statement-based
replication and supports multi-master topologies.
The same protocol extends to synchronization between solidDB and mobile devices running the IBM Mobile
Database. For synchronization between solidDB and other data servers, log-based replication is used. The
solidDB database can cache data from a back-end database, for example, to improve application performance
using local in-memory processing or to save resources on a potentially very expensive back-end database server.
Caching functionality supports back-end databases from multiple vendors including IBM, Oracle and Microsoft.
The Hot-Standby functionality in solidDB combines with the in-memory technology to provide durability
with high performance. This is discussed in more detail is section 3.
2 Makings of solidDB in-memory technology
Database operations on solidDB in-memory tables can be extremely fast because the storage and index structures
are optimized for in-memory operation. The in-memory engine can reduce the number of instructions needed to
access the data. For example, the solidDB in-memory engine does not implement page-oriented indexes or data
structures that would introduce inherent overhead for in-page processing.
In-memory databases typically forgo the use of large-block indexes, sometimes called bushy trees, in favor
of slimmer structures (tall trees) where the number of index levels is increased and the index node size is kept
to a minimum to avoid costly in-node processing. IBM solidDB uses an index called trie  that was originally
created for text searching but is well suited for in-memory indexing. The Vtrie and index concurrency control
designs are discussed in more detail in sections 2.1 and 2.3.
The solidDB in-memory technology is further enhanced by checkpoint execution. A checkpoint is a persis-
tent image of the whole database, allowing the database to be recovered after a system crash or other cases of
down time. IBM solidDB creates a snapshot-consistent checkpoint that is alone sufﬁcient to recover the database
to a consistent state that existed at some point in the past.
2.1 Vtrie indexes
The basic index structure in the in-memory engine is a Vtrie (variable length trie) that is optimized variation
of path- and level-compressed trie. Vtrie is built on top of leaf node level very much similar to those used in
B+-trees. Key values are encoded in such a way that comparison of corresponding key parts can use bytewise
comparison and end of keypart evaluates smaller than any byte value. Each Vtrie node can point to maximum
257 child nodes, one for each byte value plus “end of keypart”. Vtrie index stores the lowest key value of each
leaf node, which guarantees the Vtrie index will not occupy excessive space even if the key value distribution
would cause low branching factor. The worst case branching factor for Vtrie is 2 which occurs when key values
are strings consisting of only two possible byte values. The B+-tree -like leaf level guarantees the Vtrie still has
far fewer nodes than there are stored key values in the index.
There are two types of key values stored in the index: routing and row keys. Routing keys are stored in Vtrie
nodes, while row keys are stored at the leaf level, which consists of an ordered list of leaf nodes. Every leaf node
is pointed to by one routing key stored in the Vtrie. The value of the routing key is higher than the high key of
the node’s left sibling and at most the same as the low key of the leaf node it points to.
Leaf node size varies depending on the keys stored to it but the default size is a few cache lines. Unlike
B-, and binary trees Vtrie does not execute any comparisons during tree traversal. Each part of a key is applied
as an array index to a pointer array of a child node. Contrary to a value comparison, array lookup is a fast
operation if the array is cached in processor caches. When individual array index is sparsely populated, it is
compressed to avoid unnecessary cache misses. Finally, on the leaf level, row key lookup is performed by
scanning preﬁx-compressed keys in cache-aligned leaf node.
2.2 Main-memory checkpointing
A checkpoint is a persistent image of the whole database, allowing the database to be recovered after a system
crash or other case of down time. IBM solidDB executes a snapshot-consistent checkpoint  that is alone
sufﬁcient to recover the database to a consistent state that existed at some point in the past. Other database
products do not normally allow this; the transaction log ﬁles must be used to reconstruct a consistent state.
However, solidDB allows transaction logging to be turned off, if desired. The solidDB solution is memory-
friendly because of its ability to allocate row images and row shadow images (different versions of the same
row) without using inefﬁcient block structures. Only those images that correspond to a consistent snapshot are
written to the checkpoint ﬁle, and the row shadows allow the currently executing transactions to run unrestricted
2.3 Main-memory index concurrency control
The in-memory engine’s indexing scheme  allows any number of readers to simultaneously traverse the
index structure without locking conﬂicts. A read request on an index uses an optimistic scheme that relies on
version numbers in index nodes to protect the integrity of the search path. The read-only traverse algorithm is
presented on Figure 1.
parent = root; current = root; parent version = parent.version;
traverse ongoing = TRUE
while traverse ongoing loop
current version = current.version;
if parent.version mismatch(parent version) then
child = current.child by key(key)
if current.version mismatch(current version) then
if child.terminal() then
result = child; traverse ongoing = FALSE;
parent version = current version; parent = current;
Figure 1: Index concurrency control algorithm.
A write operation on an index uses a two-level locking scheme to protect the operation against other write
To support the lock-free read-only traversal scheme, when a writer changes any index node it ﬁrst increments
the node version number (to an odd value). After the consistency of the path being modiﬁed is achieved again the
(odd) version numbers are incremented again (to even). The method version mismatch() above returns TRUE if
either of these apply:
1. node.version <> version argument OR
2. version is an odd value.
To help a read request to complete the search even if the search path is being heavily updated, a hot-spot
protection scheme is also implemented. A search that needs to run a retry several times due to a version conﬂict
falls back to locking mode that performs the traversal as if it were a write request. In practice this is very seldom
The above methods still needs some reﬁnement: When a ”child” pointer is dereferenced, two issues arise.
1. Can we safely even read the version number dereferencing the pointer?
2. Even if the pointer value is still a legal memory reference, how do we know that the version number ﬁeld
mismatches in case the memory location has been freed and then reallocated for some other purpose?
Both those issues would certainly be problems if freeing of an unused tree node would be a direct call to the
allocator’s free()-function. To resolve this issue, solidDB uses a memory manager for versioned memory objects
 that guarantees the version number ﬁeld is neither overwritten by an arbitrary value nor decremented. The
engine has a ”purge level” which is a deallocation counter value before the oldest index traversal started. When
an index traversal ends, it may increase the purge level. When the purge level increases, freed nodes can be
released to underlying memory manager. This ensures that the version-checking traversal remains safe even
when index nodes are freed. Recycling of memory to new versioned memory objects, i.e. index tree nodes, is
still possible, because unpurged freed nodes can be used as new versioned memory objects.
3 High Availability Through the Use of Hot-Standby Replication
IBM solidDB provides high availability through automated error detection and fast fail over functionality in two-
node Hot-Standby cluster (HSB, for short). HSB can be efﬁciently combined with in-memory engine because
replicating REDO logs provides failure resiliency without having to wait a single disk access before commit.
Full durability is achieved by means of asynchronous logging. Prior to active HSB replication the nodes, namely
Primary (master) and Secondary (standby, slave) establish a connection which guarantees that database in nodes
are in sync. Connection is maintained during normal operation and monitored by heartbeat mechanism. When
connected, Primary accepts write transactions, generates row-level redo log records of changes and sends them
to Secondary. Secondary secures the consistency of standby database by ﬁrst acquiring long-term locks for rows
before re-executing log operations. As a consequence, conﬂicting updates  are serialized due the use of
locks, but non-conﬂicting ones beneﬁt from parallelized, multi-threaded execution in multi-core environments.
In an HSB database, transaction logs are sent to the Secondary server by way of a replication protocol. In
order to preserve the database consistency in the presence of fail over, the replication protocol is built very much
on the same principles as physical log writing: the transaction order is preserved, and commit records denote
committed transactions. If a fail over happens, the standby server performs a similar database recovery as if
a transaction log was used: the uncommitted transactions are removed and the committed ones are queued for
Synchronous (2Safe) replication protocols provide varying balance between safety and latency . How-
ever, they all ensure that there is no single point of failure which would lose transactions. There are three 2Safe
variables available. 2Safe Received commits as soon as Secondary acknowledges that is has received transac-
tion log. 2Safe Visible and 2Safe Committed both commit when Secondary has executed and committed the
transaction, but when 2Safe Visible is used, Secondary sends acknowledgment to the Primary prior to physical
log writing. Unlike 2Safe protocols, asynchronous replication protocol (1Safe) prefers performance over safety
by committing immediately without waiting for Secondary’s response.
When solidDB server is used in Hot-Standby mode, it is state conscious: inside solidDB there is a non-
deterministic ﬁnite-state automaton, which at any moment, unambiguously deﬁnes the server’s capabilities. For
example, whether the server can execute write transactions, solidDB provides an administrative API for querying
and modifying HSB states.
solidDB also provides a High-Availability Controller (HAC, for short) software to automatically detect er-
rors in HSB and recover from them. Every HSB node includes a solidDB HSB server, and a HAC instance.
In addition to sub-second failure detection and fail over, HAC handles all single failures and several failure
sequences without downtime. Network errors are detected by using an External Reference Entity (ERE) .
External Reference Entity which can be any device sharing the network with HSB nodes, which responds to ping
command. When HSB connection gets broken, HAC ﬁrst attempts to reach the ERE to determine the correct
failure type. If ERE responds, HAC concludes that the other server has either failed or become isolated. Only
after that the local HSB server can continue (if it was Master) or start (otherwise) to execute write transactions.
4 Performance and Conclusions
In solidDB, the main focus is on short response times that naturally results from the fact that the data is already
in memory. Additional techniques are available to avoid any unnecessary latency in the local database usage,
such as linking applications with the database server code by way of special drivers. By using those approaches,
one can shorten the query response time to a fraction (one tenth or even less) of that available in a traditional
database system. The improved response time also fuels high throughput. Nevertheless, techniques of improving
the throughput of concurrent operations are applied too. The outcome is a unique combination of short response
times and high throughput.
The advantages of solidDB in-memory database over a disk-based database are illustrated in Figure 2 where
the response time in milliseconds of single primary key fetch and single row update based on the primary key is
Figure 2: solidDB single operation response time.
Figure 3 shows the results of an experiment involving a database benchmark called Telecom Application
Transaction Processing (TATP) 1that was run on a middle-range system 2platform. The TATP benchmark
simulates a typical Home Location Register (HLR) database used by a mobile carrier. The HLR is an application
mobile network operators use to store all relevant information about valid subscribers, including the mobile
phone number, the services to which they have subscribed, access privileges, and the current location of the
subscriber’s handset. Every call to and from a mobile phone involves look ups against the HLRs of both parties,
making it a perfect example of a demanding, high-throughput environment where the workloads are pertinent to
all applications requiring extreme speed: telecommunications, ﬁnancial services, gaming, event processing and
alerting, reservation systems, and so on. The benchmark generates a ﬂooding load on a database server. This
means that the load is generated up to the maximum throughput point that the server can sustain. The load is
composed of pre-deﬁned transactions run against a speciﬁed target database.
The benchmark uses four tables and a set of seven transactions that may be combined in different mixes. The
most typical mix is a combination of 80% or read transactions and 20% of modiﬁcation transactions. The TATP
benchmark has been used in industry  and research [7, 3, 8, 9, 14]. Experiment used database containing
1 million subscribers. The results of a TATP benchmark show the overall throughput of the system, measured
as the Mean Qualiﬁed Throughput (MQTh) of the target database system, in transactions per second, over the
seven transaction types. This experiment used shared memory access for clients and ﬁgure shows the scalability
of the solidDB when number of concurrent clients are increased.
Figure 3: solidDB user load scalability.
In addition to telecom solutions, solidDB has shown its strength on various other business areas where
predictable low-latency and high-throughput transactional data processing is a must, such as media delivery 
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